{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "city='Beijing'\n",
    "city_cn='北京'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Developer Salary in Beijing 北京程序员工资调查\n",
    "我在4月1日到3日之间，抓取了某招聘网站的软件和互联网类招聘数据40万条，其中通过程序判断为程序员的14万条。地域方面，我选择了24个主要城市。不过本文只以一线城市为研究对象。这样是为了和我2017年6月的数据做对比。\n",
    "\n",
    "提到2017年的文章，现在居然还有很多人，把这篇文章拿出来炒作。对于社会来说，可气的是，他们直接把2017改成2019，就发表了，这不是骗人么？！对于我来说，可气的是，他们转载居然还冒充是原创，是可忍熟不可忍！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sys\n",
    "sys.path.append('..')\n",
    "import db\n",
    "import inspect\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "import numpy as np\n",
    "import weighted\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn=db.get_conn()\n",
    "data_original=pd.read_sql(sql=\"select * from _201904v2 where monthly_salary>0 and monthly_salary<180000\", con=conn)\n",
    "conn.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_job_ids=['104660258','104142922','108434795','101357291','106253516','110368302','111391233','108665401','109277048'\n",
    "                  ,'73857191','108584955','102824950','102824949','111391233','110884556']\n",
    "data=data_original[~data_original.job_id.isin(error_job_ids)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "#del data['publish_date']\n",
    "#del data['published_on_weekend']\n",
    "#del data['title']\n",
    "#del data['title']\n",
    "#del data['company_title']\n",
    "#del data['company_description']\n",
    "#del data['job_description']\n",
    "#del data['job_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
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       "      <th>published_on_weekend</th>\n",
       "      <th>tag_baby_care</th>\n",
       "      <th>tag_five_insurance</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>25849</th>\n",
       "      <td>110969579</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>200</td>\n",
       "      <td>JAVA开发工程师（2019届应届毕业生）</td>\n",
       "      <td>一般程序员</td>\n",
       "      <td>guangzhou</td>\n",
       "      <td>广州汇智通信技术有限公司是专业从事国家特殊通信系统研制工作的大型国有控股混合所有制企业。公司...</td>\n",
       "      <td>1000-5000</td>\n",
       "      <td>广州汇智通信技术有限公司</td>\n",
       "      <td>国企</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>本科</td>\n",
       "      <td>False</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>广州汇智通信技术有限公司                                  ...</td>\n",
       "      <td>广州|无工作经验|本科|招200人|03-31发布</td>\n",
       "      <td>五险一金,补充医疗保险,补充公积金,交通补贴,年终奖金,绩效奖金,通讯补贴,定期体检,餐饮补贴</td>\n",
       "      <td>False</td>\n",
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       "      <td>2019-03-31</td>\n",
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       "      <td>108834444</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>150</td>\n",
       "      <td>Java开发工程师</td>\n",
       "      <td>一般程序员</td>\n",
       "      <td>beijing</td>\n",
       "      <td>大连华信计算机技术股份有限公司（简称大连华信）是一家面向全球客户提供领先的应用软件产品、信息...</td>\n",
       "      <td>5000-10000</td>\n",
       "      <td>大连华信计算机技术股份有限公司</td>\n",
       "      <td>合资</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>本科</td>\n",
       "      <td>False</td>\n",
       "      <td>5_10</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>1、5年以上java开发工作经验。2、有丰富研发经验；深入理解SpringCloud、spr...</td>\n",
       "      <td>北京-朝阳区|5-7年经验|本科|招150人|03-29发布</td>\n",
       "      <td>五险一金,补充医疗保险,定期体检,年终奖金</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2019-03-29</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>30861</th>\n",
       "      <td>112071980</td>\n",
       "      <td>12500.0</td>\n",
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       "      <td>Java研发工程师（WEB）-广州</td>\n",
       "      <td>一般程序员</td>\n",
       "      <td>guangzhou</td>\n",
       "      <td>美亚柏科公司简介厦门市美亚柏科信息股份有限公司（股票简称：美亚柏科，股票代码：300188）...</td>\n",
       "      <td>1000-5000</td>\n",
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       "      <td>民营公司</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>本科</td>\n",
       "      <td>False</td>\n",
       "      <td>3_5</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>工作职责:1)\\t基于海量数据的WEB应用研发。2)\\t数据可视化设计与实现。3)\\t根据业...</td>\n",
       "      <td>广州-海珠区|3-4年经验|本科|招150人|04-01发布</td>\n",
       "      <td>五险一金,免费班车,员工旅游,交通补贴,餐饮补贴,年终奖金,弹性工作,股票期权</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>110864187</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>150</td>\n",
       "      <td>初级测试工程师</td>\n",
       "      <td>一般程序员</td>\n",
       "      <td>beijing</td>\n",
       "      <td>···</td>\n",
       "      <td>50-150</td>\n",
       "      <td>北京领航绿色科技有限公司</td>\n",
       "      <td>民营公司</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>大专</td>\n",
       "      <td>False</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>finance</td>\n",
       "      <td>False</td>\n",
       "      <td>工作职业一;1.根据软件测试计划和测试方案，设计测试数据和测试用例。            ...</td>\n",
       "      <td>北京-朝阳区|无工作经验|大专|招150人|03-26发布</td>\n",
       "      <td>五险一金,免费班车,交通补贴,餐饮补贴,绩效奖金</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2019-03-26</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30797</th>\n",
       "      <td>112055617</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>100</td>\n",
       "      <td>Java开发工程师</td>\n",
       "      <td>一般程序员</td>\n",
       "      <td>guangzhou</td>\n",
       "      <td>贵州四方合创科技有限公司主营业务是围绕运营商，面向政府、企业用户的信息服务需求，提供可运营的...</td>\n",
       "      <td>50-150</td>\n",
       "      <td>贵州四方合创科技有限公司</td>\n",
       "      <td>民营公司</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>本科</td>\n",
       "      <td>False</td>\n",
       "      <td>1_3</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>computer</td>\n",
       "      <td>False</td>\n",
       "      <td>技能要求：J2EE，Linux，ASP，node.js岗位要求： 1.计算机、信管等相关专业...</td>\n",
       "      <td>贵阳|1年经验|本科|招100人|04-01发布</td>\n",
       "      <td>五险一金,绩效奖金,年终奖金,股票期权,弹性工作,定期体检,周末双休,多次调薪,员工旅游</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          job_id  monthly_salary  headcount                  title career  \\\n",
       "25849  110969579         12500.0        200  JAVA开发工程师（2019届应届毕业生）  一般程序员   \n",
       "15903  108834444         15000.0        150              Java开发工程师  一般程序员   \n",
       "30861  112071980         12500.0        150      Java研发工程师（WEB）-广州  一般程序员   \n",
       "25365  110864187          7000.0        150                初级测试工程师  一般程序员   \n",
       "30797  112055617          8000.0        100              Java开发工程师  一般程序员   \n",
       "\n",
       "            city                                company_description  \\\n",
       "25849  guangzhou  广州汇智通信技术有限公司是专业从事国家特殊通信系统研制工作的大型国有控股混合所有制企业。公司...   \n",
       "15903    beijing  大连华信计算机技术股份有限公司（简称大连华信）是一家面向全球客户提供领先的应用软件产品、信息...   \n",
       "30861  guangzhou  美亚柏科公司简介厦门市美亚柏科信息股份有限公司（股票简称：美亚柏科，股票代码：300188）...   \n",
       "25365    beijing                                                ···   \n",
       "30797  guangzhou  贵州四方合创科技有限公司主营业务是围绕运营商，面向政府、企业用户的信息服务需求，提供可运营的...   \n",
       "\n",
       "      company_size    company_title company_type  ageism  db_Apache_Hive  \\\n",
       "25849    1000-5000     广州汇智通信技术有限公司           国企   False           False   \n",
       "15903   5000-10000  大连华信计算机技术股份有限公司           合资   False           False   \n",
       "30861    1000-5000  厦门市美亚柏科信息股份有限公司         民营公司   False           False   \n",
       "25365       50-150     北京领航绿色科技有限公司         民营公司   False           False   \n",
       "30797       50-150     贵州四方合创科技有限公司         民营公司   False           False   \n",
       "\n",
       "       db_CouchBase  db_CouchDB  db_DB2  db_DynamoDB  db_Elasticsearch  \\\n",
       "25849         False       False   False        False             False   \n",
       "15903         False       False   False        False             False   \n",
       "30861         False       False   False        False             False   \n",
       "25365         False       False   False        False             False   \n",
       "30797         False       False    True        False             False   \n",
       "\n",
       "       db_FileMaker  db_Firebase  db_Firebird  db_Hbase  db_Informix  \\\n",
       "25849         False        False        False     False        False   \n",
       "15903         False        False        False     False        False   \n",
       "30861         False        False        False     False        False   \n",
       "25365         False        False        False     False        False   \n",
       "30797         False        False        False     False        False   \n",
       "\n",
       "       db_Ingres  db_MariaDB  db_Memcached  db_MongoDB  db_MySQL  db_Neo4j  \\\n",
       "25849      False       False         False       False     False     False   \n",
       "15903      False       False         False       False      True     False   \n",
       "30861      False       False         False       False     False     False   \n",
       "25365      False       False         False       False      True     False   \n",
       "30797      False       False         False       False     False     False   \n",
       "\n",
       "       db_Netezza  db_Oracle  db_PostgreSQL  db_Redis  db_Riak  db_SAP_HANA  \\\n",
       "25849       False      False          False     False    False        False   \n",
       "15903       False       True          False     False    False        False   \n",
       "30861       False      False          False     False    False        False   \n",
       "25365       False      False          False     False    False        False   \n",
       "30797       False      False          False     False    False        False   \n",
       "\n",
       "       db_SQL_Server  db_SQLite  db_Solr  db_Splunk  db_Sybase  db_Teradata  \\\n",
       "25849          False      False    False      False      False        False   \n",
       "15903          False      False    False      False      False        False   \n",
       "30861          False      False    False      False      False        False   \n",
       "25365          False      False    False      False      False        False   \n",
       "30797           True      False    False      False      False        False   \n",
       "\n",
       "       db_dBase edu  english experience  expert_adas  expert_blockchain  \\\n",
       "25849     False  本科    False         no        False              False   \n",
       "15903     False  本科    False       5_10        False              False   \n",
       "30861     False  本科    False        3_5        False              False   \n",
       "25365     False  大专    False         no        False              False   \n",
       "30797     False  本科    False        1_3        False              False   \n",
       "\n",
       "       expert_embed  expert_expert  expert_gis  _996_yes  _996_no  industry  \\\n",
       "25849         False          False       False      True    False  computer   \n",
       "15903         False          False       False     False    False  computer   \n",
       "30861         False          False       False     False    False  computer   \n",
       "25365         False          False       False     False     True   finance   \n",
       "30797         False          False       False     False     True  computer   \n",
       "\n",
       "       japanese                                    job_description  \\\n",
       "25849     False  广州汇智通信技术有限公司                                  ...   \n",
       "15903     False  1、5年以上java开发工作经验。2、有丰富研发经验；深入理解SpringCloud、spr...   \n",
       "30861     False  工作职责:1)\\t基于海量数据的WEB应用研发。2)\\t数据可视化设计与实现。3)\\t根据业...   \n",
       "25365     False  工作职业一;1.根据软件测试计划和测试方案，设计测试数据和测试用例。            ...   \n",
       "30797     False  技能要求：J2EE，Linux，ASP，node.js岗位要求： 1.计算机、信管等相关专业...   \n",
       "\n",
       "                          job_summary  \\\n",
       "25849       广州|无工作经验|本科|招200人|03-31发布   \n",
       "15903  北京-朝阳区|5-7年经验|本科|招150人|03-29发布   \n",
       "30861  广州-海珠区|3-4年经验|本科|招150人|04-01发布   \n",
       "25365   北京-朝阳区|无工作经验|大专|招150人|03-26发布   \n",
       "30797        贵阳|1年经验|本科|招100人|04-01发布   \n",
       "\n",
       "                                              job_tags  phone_android  \\\n",
       "25849  五险一金,补充医疗保险,补充公积金,交通补贴,年终奖金,绩效奖金,通讯补贴,定期体检,餐饮补贴          False   \n",
       "15903                            五险一金,补充医疗保险,定期体检,年终奖金          False   \n",
       "30861          五险一金,免费班车,员工旅游,交通补贴,餐饮补贴,年终奖金,弹性工作,股票期权          False   \n",
       "25365                         五险一金,免费班车,交通补贴,餐饮补贴,绩效奖金          False   \n",
       "30797     五险一金,绩效奖金,年终奖金,股票期权,弹性工作,定期体检,周末双休,多次调薪,员工旅游          False   \n",
       "\n",
       "       phone_app  phone_iso  pl_c_sharp  pl_cpp  pl_delphi  pl_go  pl_haskell  \\\n",
       "25849       True       True        True    True      False  False       False   \n",
       "15903      False      False       False   False      False   True       False   \n",
       "30861      False      False       False   False      False  False       False   \n",
       "25365      False      False       False   False      False  False       False   \n",
       "30797      False      False       False   False      False  False       False   \n",
       "\n",
       "       pl_java  pl_javascript  pl_julia  pl_kotlin  pl_lua  pl_matlab  \\\n",
       "25849     True          False     False      False   False      False   \n",
       "15903     True          False     False      False   False      False   \n",
       "30861     True           True     False      False   False      False   \n",
       "25365    False          False     False      False   False      False   \n",
       "30797     True          False     False      False   False      False   \n",
       "\n",
       "       pl_objective_c  pl_perl  pl_php  pl_python  pl_ruby  pl_rust  \\\n",
       "25849           False    False   False      False    False    False   \n",
       "15903           False    False   False       True    False    False   \n",
       "30861           False    False   False      False    False    False   \n",
       "25365           False    False   False      False    False    False   \n",
       "30797           False    False   False      False    False    False   \n",
       "\n",
       "       pl_scrala  pl_swift  pl_typescript  pl_vba  pl_visual_basic  \\\n",
       "25849      False     False          False   False            False   \n",
       "15903      False     False          False   False            False   \n",
       "30861      False     False          False   False            False   \n",
       "25365      False     False          False   False            False   \n",
       "30797      False     False          False   False            False   \n",
       "\n",
       "      publish_date  published_on_weekend  tag_baby_care  tag_five_insurance  \\\n",
       "25849   2019-03-31                  True          False                True   \n",
       "15903   2019-03-29                 False          False                True   \n",
       "30861   2019-04-01                 False          False                True   \n",
       "25365   2019-03-26                 False          False                True   \n",
       "30797   2019-04-01                 False          False                True   \n",
       "\n",
       "       tag_flexible  tag_no_overtime  tag_rest_one_day  tag_rest_two_days  \\\n",
       "25849         False            False             False              False   \n",
       "15903         False            False             False              False   \n",
       "30861          True            False             False              False   \n",
       "25365         False            False             False              False   \n",
       "30797          True            False             False               True   \n",
       "\n",
       "       tag_stock  \n",
       "25849      False  \n",
       "15903      False  \n",
       "30861       True  \n",
       "25365      False  \n",
       "30797       True  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=data.sort_values(by='headcount', ascending=False)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pd_weighted_mean(group, avg_name, weight_name):\n",
    "    \"\"\" http://stackoverflow.com/questions/10951341/pandas-dataframe-aggregate-function-using-multiple-columns\n",
    "    In rare instance, we may not have weights, so just return the mean. Customize this if your business case\n",
    "    should return otherwise.\n",
    "    \"\"\"\n",
    "    d = group[avg_name]\n",
    "    w = group[weight_name]\n",
    "    try:\n",
    "        return (d * w).sum() / w.sum()\n",
    "    except ZeroDivisionError:\n",
    "        return d.mean()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Unilateral Stats 总体统计"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有数据可知，程序员向一线城市集中的趋势非常明显。\n",
    "\n",
    "According to the statistics, significant amount of developers are in the first tier cities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4250., 10000., 12500., 18000., 35000.])"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salary_mean=weighted.weighted_mean(data.monthly_salary.values, data.headcount.values)\n",
    "q=weighted.weighted_quantile(data.monthly_salary.values,[0.025,0.25,0.5,0.75,0.975], data.headcount.values)\n",
    "q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019年北京程序员的平均工资为15314元，工资中位数为12500元，其中95%的人的工资位于4250到35000元之间。\n"
     ]
    }
   ],
   "source": [
    "print('2019年{}程序员的平均工资为{:.0f}元，工资中位数为{:.0f}元，其中95%的人的工资位于{:.0f}到{:.0f}元之间。'\n",
    "      .format(city_cn,salary_mean, q[2], q[0], q[4]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In 2019, Developers in Beijing earn 15314 Yuan as average, the median is 12500 Yuan, 95% of them earn between 4250 and 35000 Yuan.\n"
     ]
    }
   ],
   "source": [
    "print('In 2019, Developers in {} earn {:.0f} Yuan as average, '\n",
    "      'the median is {:.0f} Yuan, 95% of them earn between {:.0f} and {:.0f} Yuan.'\n",
    "      .format(city,salary_mean, q[2], q[0], q[4]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12c313422b0>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.monthly_salary.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It does not look like normal distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NormaltestResult(statistic=32378.436582408962, pvalue=0.0)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.normaltest(data.monthly_salary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "null hypothesis: x comes from a normal distribution\n",
    "    \n",
    "p=0\n",
    "\n",
    "The null hypothesis can be rejected\n",
    "\n",
    "conclusion: data is not normally distributed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Zoom in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[data.monthly_salary<40000].monthly_salary.hist()\n",
    "plt.annotate('https://github.com/juwikuang/job_survey', xy=(0,0), xytext=(2000, 100), color='white')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Role 角色"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_sub_stats_by_col(data, col):\n",
    "    categories=data[col].unique()\n",
    "    salary_mean=[]\n",
    "    salary_95_min=[]\n",
    "    salary_95_max=[]\n",
    "    salary_median=[]\n",
    "\n",
    "    count=[]\n",
    "    \n",
    "    categorys_out=[]\n",
    "    for category in categories:\n",
    "        #print(feature)\n",
    "        idata=data[data[col]==category]\n",
    "        headcount=idata.headcount.sum()\n",
    "        values = idata.monthly_salary.values\n",
    "        weights = idata.headcount.values\n",
    "        #print(str(headcount))\n",
    "        if headcount==0:\n",
    "            continue\n",
    "        \n",
    "        salary_mean.append(np.average(values, weights=weights))\n",
    "        \n",
    "\n",
    "        q = weighted.weighted_quantile(values,[0.025,0.5,0.975],weights)\n",
    "        salary_95_min.append(q[0])\n",
    "        salary_median.append(q[1])\n",
    "        salary_95_max.append(q[2])\n",
    "        count.append(idata.headcount.sum())\n",
    "        categorys_out.append(category)\n",
    "    sub_data=pd.DataFrame()\n",
    "    sub_data[col]=[c for c in categorys_out]\n",
    "    sub_data['salary_mean']=salary_mean\n",
    "    sub_data['salary_95_min']=salary_95_min\n",
    "    sub_data['salary_median']=salary_median\n",
    "    sub_data['salary_95_max']=salary_95_max\n",
    "    sub_data['head_count']=count\n",
    "    sub_data['percentage']=count/np.sum(count)\n",
    "    sub_data=sub_data.sort_values(by='salary_mean', ascending=False)\n",
    "\n",
    "    return sub_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_83772b94_5d03_11e9_9315_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >career</th> \n",
       "        <th class=\"col_heading level0 col1\" >salary_mean</th> \n",
       "        <th class=\"col_heading level0 col2\" >salary_95_min</th> \n",
       "        <th class=\"col_heading level0 col3\" >salary_median</th> \n",
       "        <th class=\"col_heading level0 col4\" >salary_95_max</th> \n",
       "        <th class=\"col_heading level0 col5\" >head_count</th> \n",
       "        <th class=\"col_heading level0 col6\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_83772b94_5d03_11e9_9315_701ce71031eflevel0_row0\" class=\"row_heading level0 row0\" >1</th> \n",
       "        <td id=\"T_83772b94_5d03_11e9_9315_701ce71031efrow0_col0\" class=\"data row0 col0\" >算法工程师</td> \n",
       "        <td id=\"T_83772b94_5d03_11e9_9315_701ce71031efrow0_col1\" class=\"data row0 col1\" >23013</td> \n",
       "        <td id=\"T_83772b94_5d03_11e9_9315_701ce71031efrow0_col2\" class=\"data row0 col2\" >5250</td> \n",
       "        <td id=\"T_83772b94_5d03_11e9_9315_701ce71031efrow0_col3\" class=\"data row0 col3\" >20000</td> \n",
       "        <td id=\"T_83772b94_5d03_11e9_9315_701ce71031efrow0_col4\" class=\"data row0 col4\" >62500</td> \n",
       "        <td id=\"T_83772b94_5d03_11e9_9315_701ce71031efrow0_col5\" class=\"data row0 col5\" >12885</td> \n",
       "        <td id=\"T_83772b94_5d03_11e9_9315_701ce71031efrow0_col6\" class=\"data row0 col6\" >9.76%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_83772b94_5d03_11e9_9315_701ce71031eflevel0_row1\" class=\"row_heading level0 row1\" >2</th> \n",
       "        <td id=\"T_83772b94_5d03_11e9_9315_701ce71031efrow1_col0\" class=\"data row1 col0\" >系统架构师</td> \n",
       "        <td id=\"T_83772b94_5d03_11e9_9315_701ce71031efrow1_col1\" class=\"data row1 col1\" >22952</td> \n",
       "        <td id=\"T_83772b94_5d03_11e9_9315_701ce71031efrow1_col2\" class=\"data row1 col2\" >6500</td> \n",
       "        <td id=\"T_83772b94_5d03_11e9_9315_701ce71031efrow1_col3\" class=\"data row1 col3\" >22500</td> \n",
       "        <td id=\"T_83772b94_5d03_11e9_9315_701ce71031efrow1_col4\" class=\"data row1 col4\" >45833.3</td> \n",
       "        <td id=\"T_83772b94_5d03_11e9_9315_701ce71031efrow1_col5\" class=\"data row1 col5\" >4764</td> \n",
       "        <td id=\"T_83772b94_5d03_11e9_9315_701ce71031efrow1_col6\" class=\"data row1 col6\" >3.61%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_83772b94_5d03_11e9_9315_701ce71031eflevel0_row2\" class=\"row_heading level0 row2\" >3</th> \n",
       "        <td id=\"T_83772b94_5d03_11e9_9315_701ce71031efrow2_col0\" class=\"data row2 col0\" >架构设计师</td> \n",
       "        <td id=\"T_83772b94_5d03_11e9_9315_701ce71031efrow2_col1\" class=\"data row2 col1\" >21308</td> \n",
       "        <td id=\"T_83772b94_5d03_11e9_9315_701ce71031efrow2_col2\" class=\"data row2 col2\" >4542.5</td> \n",
       "        <td id=\"T_83772b94_5d03_11e9_9315_701ce71031efrow2_col3\" class=\"data row2 col3\" >19000</td> \n",
       "        <td id=\"T_83772b94_5d03_11e9_9315_701ce71031efrow2_col4\" class=\"data row2 col4\" >50000</td> \n",
       "        <td id=\"T_83772b94_5d03_11e9_9315_701ce71031efrow2_col5\" class=\"data row2 col5\" >1877</td> \n",
       "        <td id=\"T_83772b94_5d03_11e9_9315_701ce71031efrow2_col6\" class=\"data row2 col6\" >1.42%</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_83772b94_5d03_11e9_9315_701ce71031eflevel0_row3\" class=\"row_heading level0 row3\" >0</th> \n",
       "        <td id=\"T_83772b94_5d03_11e9_9315_701ce71031efrow3_col0\" class=\"data row3 col0\" >一般程序员</td> \n",
       "        <td id=\"T_83772b94_5d03_11e9_9315_701ce71031efrow3_col1\" class=\"data row3 col1\" >14009</td> \n",
       "        <td id=\"T_83772b94_5d03_11e9_9315_701ce71031efrow3_col2\" class=\"data row3 col2\" >4000</td> \n",
       "        <td id=\"T_83772b94_5d03_11e9_9315_701ce71031efrow3_col3\" class=\"data row3 col3\" >12500</td> \n",
       "        <td id=\"T_83772b94_5d03_11e9_9315_701ce71031efrow3_col4\" class=\"data row3 col4\" >30000</td> \n",
       "        <td id=\"T_83772b94_5d03_11e9_9315_701ce71031efrow3_col5\" class=\"data row3 col5\" >112534</td> \n",
       "        <td id=\"T_83772b94_5d03_11e9_9315_701ce71031efrow3_col6\" class=\"data row3 col6\" >85.21%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x12c314aca90>"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_career = get_sub_stats_by_col(data,'career')\n",
    "data_career.style.format({\"salary_mean\":\"{:.0f}\",\"salary_median\":\"{:.0f}\",\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15313.812534706447"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(data.monthly_salary * data.headcount) / data.headcount.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "132060"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.headcount.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    40761.000000\n",
       "mean         3.239862\n",
       "std          3.647367\n",
       "min          1.000000\n",
       "25%          1.000000\n",
       "50%          2.000000\n",
       "75%          5.000000\n",
       "max        200.000000\n",
       "Name: headcount, dtype: float64"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.headcount.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job_id</th>\n",
       "      <th>monthly_salary</th>\n",
       "      <th>headcount</th>\n",
       "      <th>title</th>\n",
       "      <th>career</th>\n",
       "      <th>city</th>\n",
       "      <th>company_description</th>\n",
       "      <th>company_size</th>\n",
       "      <th>company_title</th>\n",
       "      <th>company_type</th>\n",
       "      <th>ageism</th>\n",
       "      <th>db_Apache_Hive</th>\n",
       "      <th>db_CouchBase</th>\n",
       "      <th>db_CouchDB</th>\n",
       "      <th>db_DB2</th>\n",
       "      <th>db_DynamoDB</th>\n",
       "      <th>db_Elasticsearch</th>\n",
       "      <th>db_FileMaker</th>\n",
       "      <th>db_Firebase</th>\n",
       "      <th>db_Firebird</th>\n",
       "      <th>db_Hbase</th>\n",
       "      <th>db_Informix</th>\n",
       "      <th>db_Ingres</th>\n",
       "      <th>db_MariaDB</th>\n",
       "      <th>db_Memcached</th>\n",
       "      <th>db_MongoDB</th>\n",
       "      <th>db_MySQL</th>\n",
       "      <th>db_Neo4j</th>\n",
       "      <th>db_Netezza</th>\n",
       "      <th>db_Oracle</th>\n",
       "      <th>db_PostgreSQL</th>\n",
       "      <th>db_Redis</th>\n",
       "      <th>db_Riak</th>\n",
       "      <th>db_SAP_HANA</th>\n",
       "      <th>db_SQL_Server</th>\n",
       "      <th>db_SQLite</th>\n",
       "      <th>db_Solr</th>\n",
       "      <th>db_Splunk</th>\n",
       "      <th>db_Sybase</th>\n",
       "      <th>db_Teradata</th>\n",
       "      <th>db_dBase</th>\n",
       "      <th>edu</th>\n",
       "      <th>english</th>\n",
       "      <th>experience</th>\n",
       "      <th>expert_adas</th>\n",
       "      <th>expert_blockchain</th>\n",
       "      <th>expert_embed</th>\n",
       "      <th>expert_expert</th>\n",
       "      <th>expert_gis</th>\n",
       "      <th>_996_yes</th>\n",
       "      <th>_996_no</th>\n",
       "      <th>industry</th>\n",
       "      <th>japanese</th>\n",
       "      <th>job_description</th>\n",
       "      <th>job_summary</th>\n",
       "      <th>job_tags</th>\n",
       "      <th>phone_android</th>\n",
       "      <th>phone_app</th>\n",
       "      <th>phone_iso</th>\n",
       "      <th>pl_c_sharp</th>\n",
       "      <th>pl_cpp</th>\n",
       "      <th>pl_delphi</th>\n",
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       "      <th>pl_haskell</th>\n",
       "      <th>pl_java</th>\n",
       "      <th>pl_javascript</th>\n",
       "      <th>pl_julia</th>\n",
       "      <th>pl_kotlin</th>\n",
       "      <th>pl_lua</th>\n",
       "      <th>pl_matlab</th>\n",
       "      <th>pl_objective_c</th>\n",
       "      <th>pl_perl</th>\n",
       "      <th>pl_php</th>\n",
       "      <th>pl_python</th>\n",
       "      <th>pl_ruby</th>\n",
       "      <th>pl_rust</th>\n",
       "      <th>pl_scrala</th>\n",
       "      <th>pl_swift</th>\n",
       "      <th>pl_typescript</th>\n",
       "      <th>pl_vba</th>\n",
       "      <th>pl_visual_basic</th>\n",
       "      <th>publish_date</th>\n",
       "      <th>published_on_weekend</th>\n",
       "      <th>tag_baby_care</th>\n",
       "      <th>tag_five_insurance</th>\n",
       "      <th>tag_flexible</th>\n",
       "      <th>tag_no_overtime</th>\n",
       "      <th>tag_rest_one_day</th>\n",
       "      <th>tag_rest_two_days</th>\n",
       "      <th>tag_stock</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25849</th>\n",
       "      <td>110969579</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>200</td>\n",
       "      <td>JAVA开发工程师（2019届应届毕业生）</td>\n",
       "      <td>一般程序员</td>\n",
       "      <td>guangzhou</td>\n",
       "      <td>广州汇智通信技术有限公司是专业从事国家特殊通信系统研制工作的大型国有控股混合所有制企业。公司...</td>\n",
       "      <td>1000-5000</td>\n",
       "      <td>广州汇智通信技术有限公司</td>\n",
       "      <td>国企</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>本科</td>\n",
       "      <td>False</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>computer</td>\n",
       "      <td>False</td>\n",
       "      <td>广州汇智通信技术有限公司                                  ...</td>\n",
       "      <td>广州|无工作经验|本科|招200人|03-31发布</td>\n",
       "      <td>五险一金,补充医疗保险,补充公积金,交通补贴,年终奖金,绩效奖金,通讯补贴,定期体检,餐饮补贴</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2019-03-31</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30861</th>\n",
       "      <td>112071980</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>150</td>\n",
       "      <td>Java研发工程师（WEB）-广州</td>\n",
       "      <td>一般程序员</td>\n",
       "      <td>guangzhou</td>\n",
       "      <td>美亚柏科公司简介厦门市美亚柏科信息股份有限公司（股票简称：美亚柏科，股票代码：300188）...</td>\n",
       "      <td>1000-5000</td>\n",
       "      <td>厦门市美亚柏科信息股份有限公司</td>\n",
       "      <td>民营公司</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>本科</td>\n",
       "      <td>False</td>\n",
       "      <td>3_5</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>computer</td>\n",
       "      <td>False</td>\n",
       "      <td>工作职责:1)\\t基于海量数据的WEB应用研发。2)\\t数据可视化设计与实现。3)\\t根据业...</td>\n",
       "      <td>广州-海珠区|3-4年经验|本科|招150人|04-01发布</td>\n",
       "      <td>五险一金,免费班车,员工旅游,交通补贴,餐饮补贴,年终奖金,弹性工作,股票期权</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25365</th>\n",
       "      <td>110864187</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>150</td>\n",
       "      <td>初级测试工程师</td>\n",
       "      <td>一般程序员</td>\n",
       "      <td>beijing</td>\n",
       "      <td>···</td>\n",
       "      <td>50-150</td>\n",
       "      <td>北京领航绿色科技有限公司</td>\n",
       "      <td>民营公司</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>大专</td>\n",
       "      <td>False</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>finance</td>\n",
       "      <td>False</td>\n",
       "      <td>工作职业一;1.根据软件测试计划和测试方案，设计测试数据和测试用例。            ...</td>\n",
       "      <td>北京-朝阳区|无工作经验|大专|招150人|03-26发布</td>\n",
       "      <td>五险一金,免费班车,交通补贴,餐饮补贴,绩效奖金</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2019-03-26</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15903</th>\n",
       "      <td>108834444</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>150</td>\n",
       "      <td>Java开发工程师</td>\n",
       "      <td>一般程序员</td>\n",
       "      <td>beijing</td>\n",
       "      <td>大连华信计算机技术股份有限公司（简称大连华信）是一家面向全球客户提供领先的应用软件产品、信息...</td>\n",
       "      <td>5000-10000</td>\n",
       "      <td>大连华信计算机技术股份有限公司</td>\n",
       "      <td>合资</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>本科</td>\n",
       "      <td>False</td>\n",
       "      <td>5_10</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>computer</td>\n",
       "      <td>False</td>\n",
       "      <td>1、5年以上java开发工作经验。2、有丰富研发经验；深入理解SpringCloud、spr...</td>\n",
       "      <td>北京-朝阳区|5-7年经验|本科|招150人|03-29发布</td>\n",
       "      <td>五险一金,补充医疗保险,定期体检,年终奖金</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2019-03-29</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30797</th>\n",
       "      <td>112055617</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>100</td>\n",
       "      <td>Java开发工程师</td>\n",
       "      <td>一般程序员</td>\n",
       "      <td>guangzhou</td>\n",
       "      <td>贵州四方合创科技有限公司主营业务是围绕运营商，面向政府、企业用户的信息服务需求，提供可运营的...</td>\n",
       "      <td>50-150</td>\n",
       "      <td>贵州四方合创科技有限公司</td>\n",
       "      <td>民营公司</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>本科</td>\n",
       "      <td>False</td>\n",
       "      <td>1_3</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>computer</td>\n",
       "      <td>False</td>\n",
       "      <td>技能要求：J2EE，Linux，ASP，node.js岗位要求： 1.计算机、信管等相关专业...</td>\n",
       "      <td>贵阳|1年经验|本科|招100人|04-01发布</td>\n",
       "      <td>五险一金,绩效奖金,年终奖金,股票期权,弹性工作,定期体检,周末双休,多次调薪,员工旅游</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          job_id  monthly_salary  headcount                  title career  \\\n",
       "25849  110969579         12500.0        200  JAVA开发工程师（2019届应届毕业生）  一般程序员   \n",
       "30861  112071980         12500.0        150      Java研发工程师（WEB）-广州  一般程序员   \n",
       "25365  110864187          7000.0        150                初级测试工程师  一般程序员   \n",
       "15903  108834444         15000.0        150              Java开发工程师  一般程序员   \n",
       "30797  112055617          8000.0        100              Java开发工程师  一般程序员   \n",
       "\n",
       "            city                                company_description  \\\n",
       "25849  guangzhou  广州汇智通信技术有限公司是专业从事国家特殊通信系统研制工作的大型国有控股混合所有制企业。公司...   \n",
       "30861  guangzhou  美亚柏科公司简介厦门市美亚柏科信息股份有限公司（股票简称：美亚柏科，股票代码：300188）...   \n",
       "25365    beijing                                                ···   \n",
       "15903    beijing  大连华信计算机技术股份有限公司（简称大连华信）是一家面向全球客户提供领先的应用软件产品、信息...   \n",
       "30797  guangzhou  贵州四方合创科技有限公司主营业务是围绕运营商，面向政府、企业用户的信息服务需求，提供可运营的...   \n",
       "\n",
       "      company_size    company_title company_type  ageism  db_Apache_Hive  \\\n",
       "25849    1000-5000     广州汇智通信技术有限公司           国企   False           False   \n",
       "30861    1000-5000  厦门市美亚柏科信息股份有限公司         民营公司   False           False   \n",
       "25365       50-150     北京领航绿色科技有限公司         民营公司   False           False   \n",
       "15903   5000-10000  大连华信计算机技术股份有限公司           合资   False           False   \n",
       "30797       50-150     贵州四方合创科技有限公司         民营公司   False           False   \n",
       "\n",
       "       db_CouchBase  db_CouchDB  db_DB2  db_DynamoDB  db_Elasticsearch  \\\n",
       "25849         False       False   False        False             False   \n",
       "30861         False       False   False        False             False   \n",
       "25365         False       False   False        False             False   \n",
       "15903         False       False   False        False             False   \n",
       "30797         False       False    True        False             False   \n",
       "\n",
       "       db_FileMaker  db_Firebase  db_Firebird  db_Hbase  db_Informix  \\\n",
       "25849         False        False        False     False        False   \n",
       "30861         False        False        False     False        False   \n",
       "25365         False        False        False     False        False   \n",
       "15903         False        False        False     False        False   \n",
       "30797         False        False        False     False        False   \n",
       "\n",
       "       db_Ingres  db_MariaDB  db_Memcached  db_MongoDB  db_MySQL  db_Neo4j  \\\n",
       "25849      False       False         False       False     False     False   \n",
       "30861      False       False         False       False     False     False   \n",
       "25365      False       False         False       False      True     False   \n",
       "15903      False       False         False       False      True     False   \n",
       "30797      False       False         False       False     False     False   \n",
       "\n",
       "       db_Netezza  db_Oracle  db_PostgreSQL  db_Redis  db_Riak  db_SAP_HANA  \\\n",
       "25849       False      False          False     False    False        False   \n",
       "30861       False      False          False     False    False        False   \n",
       "25365       False      False          False     False    False        False   \n",
       "15903       False       True          False     False    False        False   \n",
       "30797       False      False          False     False    False        False   \n",
       "\n",
       "       db_SQL_Server  db_SQLite  db_Solr  db_Splunk  db_Sybase  db_Teradata  \\\n",
       "25849          False      False    False      False      False        False   \n",
       "30861          False      False    False      False      False        False   \n",
       "25365          False      False    False      False      False        False   \n",
       "15903          False      False    False      False      False        False   \n",
       "30797           True      False    False      False      False        False   \n",
       "\n",
       "       db_dBase edu  english experience  expert_adas  expert_blockchain  \\\n",
       "25849     False  本科    False         no        False              False   \n",
       "30861     False  本科    False        3_5        False              False   \n",
       "25365     False  大专    False         no        False              False   \n",
       "15903     False  本科    False       5_10        False              False   \n",
       "30797     False  本科    False        1_3        False              False   \n",
       "\n",
       "       expert_embed  expert_expert  expert_gis  _996_yes  _996_no  industry  \\\n",
       "25849         False          False       False      True    False  computer   \n",
       "30861         False          False       False     False    False  computer   \n",
       "25365         False          False       False     False     True   finance   \n",
       "15903         False          False       False     False    False  computer   \n",
       "30797         False          False       False     False     True  computer   \n",
       "\n",
       "       japanese                                    job_description  \\\n",
       "25849     False  广州汇智通信技术有限公司                                  ...   \n",
       "30861     False  工作职责:1)\\t基于海量数据的WEB应用研发。2)\\t数据可视化设计与实现。3)\\t根据业...   \n",
       "25365     False  工作职业一;1.根据软件测试计划和测试方案，设计测试数据和测试用例。            ...   \n",
       "15903     False  1、5年以上java开发工作经验。2、有丰富研发经验；深入理解SpringCloud、spr...   \n",
       "30797     False  技能要求：J2EE，Linux，ASP，node.js岗位要求： 1.计算机、信管等相关专业...   \n",
       "\n",
       "                          job_summary  \\\n",
       "25849       广州|无工作经验|本科|招200人|03-31发布   \n",
       "30861  广州-海珠区|3-4年经验|本科|招150人|04-01发布   \n",
       "25365   北京-朝阳区|无工作经验|大专|招150人|03-26发布   \n",
       "15903  北京-朝阳区|5-7年经验|本科|招150人|03-29发布   \n",
       "30797        贵阳|1年经验|本科|招100人|04-01发布   \n",
       "\n",
       "                                              job_tags  phone_android  \\\n",
       "25849  五险一金,补充医疗保险,补充公积金,交通补贴,年终奖金,绩效奖金,通讯补贴,定期体检,餐饮补贴          False   \n",
       "30861          五险一金,免费班车,员工旅游,交通补贴,餐饮补贴,年终奖金,弹性工作,股票期权          False   \n",
       "25365                         五险一金,免费班车,交通补贴,餐饮补贴,绩效奖金          False   \n",
       "15903                            五险一金,补充医疗保险,定期体检,年终奖金          False   \n",
       "30797     五险一金,绩效奖金,年终奖金,股票期权,弹性工作,定期体检,周末双休,多次调薪,员工旅游          False   \n",
       "\n",
       "       phone_app  phone_iso  pl_c_sharp  pl_cpp  pl_delphi  pl_go  pl_haskell  \\\n",
       "25849       True       True        True    True      False  False       False   \n",
       "30861      False      False       False   False      False  False       False   \n",
       "25365      False      False       False   False      False  False       False   \n",
       "15903      False      False       False   False      False   True       False   \n",
       "30797      False      False       False   False      False  False       False   \n",
       "\n",
       "       pl_java  pl_javascript  pl_julia  pl_kotlin  pl_lua  pl_matlab  \\\n",
       "25849     True          False     False      False   False      False   \n",
       "30861     True           True     False      False   False      False   \n",
       "25365    False          False     False      False   False      False   \n",
       "15903     True          False     False      False   False      False   \n",
       "30797     True          False     False      False   False      False   \n",
       "\n",
       "       pl_objective_c  pl_perl  pl_php  pl_python  pl_ruby  pl_rust  \\\n",
       "25849           False    False   False      False    False    False   \n",
       "30861           False    False   False      False    False    False   \n",
       "25365           False    False   False      False    False    False   \n",
       "15903           False    False   False       True    False    False   \n",
       "30797           False    False   False      False    False    False   \n",
       "\n",
       "       pl_scrala  pl_swift  pl_typescript  pl_vba  pl_visual_basic  \\\n",
       "25849      False     False          False   False            False   \n",
       "30861      False     False          False   False            False   \n",
       "25365      False     False          False   False            False   \n",
       "15903      False     False          False   False            False   \n",
       "30797      False     False          False   False            False   \n",
       "\n",
       "      publish_date  published_on_weekend  tag_baby_care  tag_five_insurance  \\\n",
       "25849   2019-03-31                  True          False                True   \n",
       "30861   2019-04-01                 False          False                True   \n",
       "25365   2019-03-26                 False          False                True   \n",
       "15903   2019-03-29                 False          False                True   \n",
       "30797   2019-04-01                 False          False                True   \n",
       "\n",
       "       tag_flexible  tag_no_overtime  tag_rest_one_day  tag_rest_two_days  \\\n",
       "25849         False            False             False              False   \n",
       "30861          True            False             False              False   \n",
       "25365         False            False             False              False   \n",
       "15903         False            False             False              False   \n",
       "30797          True            False             False               True   \n",
       "\n",
       "       tag_stock  \n",
       "25849      False  \n",
       "30861       True  \n",
       "25365      False  \n",
       "15903      False  \n",
       "30797       True  "
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.display.max_columns=100\n",
    "data.sort_values(by='headcount', ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 编程语言"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def get_sub_stats_by_prefix(data, prefix):\n",
    "    \n",
    "    features = [feature for feature in data.columns if feature.startswith(prefix)]\n",
    "    salary_mean=[]\n",
    "    salary_median=[]\n",
    "    salary_95_min=[]\n",
    "    salary_95_max=[]\n",
    "    count=[]\n",
    "    \n",
    "    features_out=[]\n",
    "    for feature in features:\n",
    "        #print(feature)\n",
    "        idata=data[data[feature]==1]\n",
    "        headcount=idata.headcount.sum()\n",
    "        values = idata.monthly_salary.values\n",
    "        weights = idata.headcount.values\n",
    "        #print(str(headcount))\n",
    "        if headcount==0:\n",
    "            continue\n",
    "        \n",
    "        salary_mean.append(weighted.weighted_mean(values, weights))\n",
    "        q = weighted.weighted_quantile(values,[0.025,0.5,0.975],weights)\n",
    "        salary_median.append(q[1])\n",
    "        salary_95_min.append(q[0])\n",
    "        salary_95_max.append(q[2])\n",
    "        count.append(idata.headcount.sum())\n",
    "        features_out.append(feature)\n",
    "    sub_data=pd.DataFrame()\n",
    "    sub_data['rank']=range(0,len(features_out))\n",
    "    sub_data[prefix]=[f.replace(prefix,'') for f in features_out]\n",
    "    sub_data['salary_mean']=salary_mean\n",
    "    sub_data['salary_median']=salary_median\n",
    "    sub_data['salary_95_min']=salary_95_min\n",
    "    sub_data['salary_95_max']=salary_95_max\n",
    "    sub_data['head_count']=count\n",
    "    sub_data['percentage']=count/np.sum(count)\n",
    "    sub_data=sub_data.sort_values(by='salary_mean', ascending=False)\n",
    "    sub_data['rank']=range(1,len(features_out)+1)\n",
    "    #sub_data=sub_data.reset_index()\n",
    "    return sub_data\n",
    "\n",
    "def apply_style(sub_data):\n",
    "    return sub_data.style.hide_index().format(\n",
    "    {\"salary_mean\":\"{:.0f}\",\"salary_median\":\"{:.0f}\",\"salary_95_min\":\"{:.0f}\",\"salary_95_max\":\"{:.0f}\",\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"col_heading level0 col0\" >rank</th> \n",
       "        <th class=\"col_heading level0 col1\" >pl_</th> \n",
       "        <th class=\"col_heading level0 col2\" >salary_mean</th> \n",
       "        <th class=\"col_heading level0 col3\" >salary_median</th> \n",
       "        <th class=\"col_heading level0 col4\" >salary_95_min</th> \n",
       "        <th class=\"col_heading level0 col5\" >salary_95_max</th> \n",
       "        <th class=\"col_heading level0 col6\" >head_count</th> \n",
       "        <th class=\"col_heading level0 col7\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow0_col0\" class=\"data row0 col0\" >1</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow0_col1\" class=\"data row0 col1\" >haskell</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow0_col2\" class=\"data row0 col2\" >29557</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow0_col3\" class=\"data row0 col3\" >30833</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow0_col4\" class=\"data row0 col4\" >7500</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow0_col5\" class=\"data row0 col5\" >45000</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow0_col6\" class=\"data row0 col6\" >32</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow0_col7\" class=\"data row0 col7\" >0.02%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow1_col0\" class=\"data row1 col0\" >2</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow1_col1\" class=\"data row1 col1\" >julia</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow1_col2\" class=\"data row1 col2\" >26875</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow1_col3\" class=\"data row1 col3\" >26875</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow1_col4\" class=\"data row1 col4\" >17500</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow1_col5\" class=\"data row1 col5\" >30000</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow1_col6\" class=\"data row1 col6\" >4</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow1_col7\" class=\"data row1 col7\" >0.00%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow2_col0\" class=\"data row2 col0\" >3</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow2_col1\" class=\"data row2 col1\" >rust</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow2_col2\" class=\"data row2 col2\" >23724</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow2_col3\" class=\"data row2 col3\" >20000</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow2_col4\" class=\"data row2 col4\" >7150</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow2_col5\" class=\"data row2 col5\" >57046</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow2_col6\" class=\"data row2 col6\" >183</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow2_col7\" class=\"data row2 col7\" >0.10%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow3_col0\" class=\"data row3 col0\" >4</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow3_col1\" class=\"data row3 col1\" >matlab</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow3_col2\" class=\"data row3 col2\" >20174</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow3_col3\" class=\"data row3 col3\" >18000</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow3_col4\" class=\"data row3 col4\" >5250</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow3_col5\" class=\"data row3 col5\" >45000</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow3_col6\" class=\"data row3 col6\" >2961</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow3_col7\" class=\"data row3 col7\" >1.58%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow4_col0\" class=\"data row4 col0\" >5</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow4_col1\" class=\"data row4 col1\" >python</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow4_col2\" class=\"data row4 col2\" >20126</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow4_col3\" class=\"data row4 col3\" >17500</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow4_col4\" class=\"data row4 col4\" >4500</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow4_col5\" class=\"data row4 col5\" >45833</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow4_col6\" class=\"data row4 col6\" >15747</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow4_col7\" class=\"data row4 col7\" >8.39%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow5_col0\" class=\"data row5 col0\" >6</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow5_col1\" class=\"data row5 col1\" >perl</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow5_col2\" class=\"data row5 col2\" >19420</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow5_col3\" class=\"data row5 col3\" >17500</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow5_col4\" class=\"data row5 col4\" >5000</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow5_col5\" class=\"data row5 col5\" >45000</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow5_col6\" class=\"data row5 col6\" >1583</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow5_col7\" class=\"data row5 col7\" >0.84%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow6_col0\" class=\"data row6 col0\" >7</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow6_col1\" class=\"data row6 col1\" >go</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow6_col2\" class=\"data row6 col2\" >19213</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow6_col3\" class=\"data row6 col3\" >17500</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow6_col4\" class=\"data row6 col4\" >6352</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow6_col5\" class=\"data row6 col5\" >45000</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow6_col6\" class=\"data row6 col6\" >15037</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow6_col7\" class=\"data row6 col7\" >8.02%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow7_col0\" class=\"data row7 col0\" >8</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow7_col1\" class=\"data row7 col1\" >lua</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow7_col2\" class=\"data row7 col2\" >18769</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow7_col3\" class=\"data row7 col3\" >17500</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow7_col4\" class=\"data row7 col4\" >5250</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow7_col5\" class=\"data row7 col5\" >42125</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow7_col6\" class=\"data row7 col6\" >1266</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow7_col7\" class=\"data row7 col7\" >0.67%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow8_col0\" class=\"data row8 col0\" >9</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow8_col1\" class=\"data row8 col1\" >cpp</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow8_col2\" class=\"data row8 col2\" >17772</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow8_col3\" class=\"data row8 col3\" >15000</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow8_col4\" class=\"data row8 col4\" >5000</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow8_col5\" class=\"data row8 col5\" >41667</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow8_col6\" class=\"data row8 col6\" >29675</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow8_col7\" class=\"data row8 col7\" >15.82%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow9_col0\" class=\"data row9 col0\" >10</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow9_col1\" class=\"data row9 col1\" >ruby</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow9_col2\" class=\"data row9 col2\" >17722</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow9_col3\" class=\"data row9 col3\" >17500</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow9_col4\" class=\"data row9 col4\" >6175</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow9_col5\" class=\"data row9 col5\" >35000</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow9_col6\" class=\"data row9 col6\" >618</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow9_col7\" class=\"data row9 col7\" >0.33%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow10_col0\" class=\"data row10 col0\" >11</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow10_col1\" class=\"data row10 col1\" >kotlin</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow10_col2\" class=\"data row10 col2\" >16435</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow10_col3\" class=\"data row10 col3\" >14000</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow10_col4\" class=\"data row10 col4\" >7500</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow10_col5\" class=\"data row10 col5\" >30514</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow10_col6\" class=\"data row10 col6\" >352</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow10_col7\" class=\"data row10 col7\" >0.19%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow11_col0\" class=\"data row11 col0\" >12</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow11_col1\" class=\"data row11 col1\" >swift</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow11_col2\" class=\"data row11 col2\" >16180</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow11_col3\" class=\"data row11 col3\" >14500</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow11_col4\" class=\"data row11 col4\" >5250</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow11_col5\" class=\"data row11 col5\" >35000</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow11_col6\" class=\"data row11 col6\" >1256</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow11_col7\" class=\"data row11 col7\" >0.67%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow12_col0\" class=\"data row12 col0\" >13</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow12_col1\" class=\"data row12 col1\" >typescript</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow12_col2\" class=\"data row12 col2\" >15835</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow12_col3\" class=\"data row12 col3\" >15000</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow12_col4\" class=\"data row12 col4\" >7000</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow12_col5\" class=\"data row12 col5\" >32089</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow12_col6\" class=\"data row12 col6\" >543</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow12_col7\" class=\"data row12 col7\" >0.29%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow13_col0\" class=\"data row13 col0\" >14</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow13_col1\" class=\"data row13 col1\" >java</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow13_col2\" class=\"data row13 col2\" >15305</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow13_col3\" class=\"data row13 col3\" >13000</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow13_col4\" class=\"data row13 col4\" >3750</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow13_col5\" class=\"data row13 col5\" >35000</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow13_col6\" class=\"data row13 col6\" >60899</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow13_col7\" class=\"data row13 col7\" >32.46%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow14_col0\" class=\"data row14 col0\" >15</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow14_col1\" class=\"data row14 col1\" >php</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow14_col2\" class=\"data row14 col2\" >14797</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow14_col3\" class=\"data row14 col3\" >12500</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow14_col4\" class=\"data row14 col4\" >3845</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow14_col5\" class=\"data row14 col5\" >35000</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow14_col6\" class=\"data row14 col6\" >9453</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow14_col7\" class=\"data row14 col7\" >5.04%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow15_col0\" class=\"data row15 col0\" >16</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow15_col1\" class=\"data row15 col1\" >objective_c</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow15_col2\" class=\"data row15 col2\" >14121</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow15_col3\" class=\"data row15 col3\" >12500</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow15_col4\" class=\"data row15 col4\" >5892</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow15_col5\" class=\"data row15 col5\" >27500</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow15_col6\" class=\"data row15 col6\" >264</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow15_col7\" class=\"data row15 col7\" >0.14%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow16_col0\" class=\"data row16 col0\" >17</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow16_col1\" class=\"data row16 col1\" >javascript</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow16_col2\" class=\"data row16 col2\" >13195</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow16_col3\" class=\"data row16 col3\" >12500</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow16_col4\" class=\"data row16 col4\" >4000</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow16_col5\" class=\"data row16 col5\" >27500</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow16_col6\" class=\"data row16 col6\" >25056</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow16_col7\" class=\"data row16 col7\" >13.36%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow17_col0\" class=\"data row17 col0\" >18</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow17_col1\" class=\"data row17 col1\" >c_sharp</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow17_col2\" class=\"data row17 col2\" >12898</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow17_col3\" class=\"data row17 col3\" >12500</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow17_col4\" class=\"data row17 col4\" >3750</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow17_col5\" class=\"data row17 col5\" >26000</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow17_col6\" class=\"data row17 col6\" >22041</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow17_col7\" class=\"data row17 col7\" >11.75%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow18_col0\" class=\"data row18 col0\" >19</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow18_col1\" class=\"data row18 col1\" >vba</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow18_col2\" class=\"data row18 col2\" >12614</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow18_col3\" class=\"data row18 col3\" >12500</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow18_col4\" class=\"data row18 col4\" >5900</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow18_col5\" class=\"data row18 col5\" >20000</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow18_col6\" class=\"data row18 col6\" >206</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow18_col7\" class=\"data row18 col7\" >0.11%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow19_col0\" class=\"data row19 col0\" >20</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow19_col1\" class=\"data row19 col1\" >delphi</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow19_col2\" class=\"data row19 col2\" >11710</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow19_col3\" class=\"data row19 col3\" >11500</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow19_col4\" class=\"data row19 col4\" >5342</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow19_col5\" class=\"data row19 col5\" >25000</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow19_col6\" class=\"data row19 col6\" >402</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow19_col7\" class=\"data row19 col7\" >0.21%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow20_col0\" class=\"data row20 col0\" >21</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow20_col1\" class=\"data row20 col1\" >visual_basic</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow20_col2\" class=\"data row20 col2\" >10129</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow20_col3\" class=\"data row20 col3\" >8733</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow20_col4\" class=\"data row20 col4\" >6419</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow20_col5\" class=\"data row20 col5\" >23312</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow20_col6\" class=\"data row20 col6\" >29</td> \n",
       "        <td id=\"T_8837f5ae_5d03_11e9_ad2e_701ce71031efrow20_col7\" class=\"data row20 col7\" >0.02%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x12c31548d30>"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pl=get_sub_stats_by_prefix(data,'pl_')\n",
    "apply_style(data_pl)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据显示，haskell才是最赚钱的编程语言。python是主流语言里面最赚钱的，比java的工资多了3000元！vb是最不赚钱的了。其中，最赚钱的编程语言和最不赚钱的，工资居然差了2倍。所以，要选好编程语言呀！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 教育"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_28f27952_5d01_11e9_a3bf_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"col_heading level0 col0\" >edu</th> \n",
       "        <th class=\"col_heading level0 col1\" >salary_mean</th> \n",
       "        <th class=\"col_heading level0 col2\" >salary_95_min</th> \n",
       "        <th class=\"col_heading level0 col3\" >salary_median</th> \n",
       "        <th class=\"col_heading level0 col4\" >salary_95_max</th> \n",
       "        <th class=\"col_heading level0 col5\" >head_count</th> \n",
       "        <th class=\"col_heading level0 col6\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <td id=\"T_28f27952_5d01_11e9_a3bf_701ce71031efrow0_col0\" class=\"data row0 col0\" >博士</td> \n",
       "        <td id=\"T_28f27952_5d01_11e9_a3bf_701ce71031efrow0_col1\" class=\"data row0 col1\" >28191</td> \n",
       "        <td id=\"T_28f27952_5d01_11e9_a3bf_701ce71031efrow0_col2\" class=\"data row0 col2\" >12500</td> \n",
       "        <td id=\"T_28f27952_5d01_11e9_a3bf_701ce71031efrow0_col3\" class=\"data row0 col3\" >27321</td> \n",
       "        <td id=\"T_28f27952_5d01_11e9_a3bf_701ce71031efrow0_col4\" class=\"data row0 col4\" >55000</td> \n",
       "        <td id=\"T_28f27952_5d01_11e9_a3bf_701ce71031efrow0_col5\" class=\"data row0 col5\" >236</td> \n",
       "        <td id=\"T_28f27952_5d01_11e9_a3bf_701ce71031efrow0_col6\" class=\"data row0 col6\" >0.22%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_28f27952_5d01_11e9_a3bf_701ce71031efrow1_col0\" class=\"data row1 col0\" >硕士</td> \n",
       "        <td id=\"T_28f27952_5d01_11e9_a3bf_701ce71031efrow1_col1\" class=\"data row1 col1\" >22426</td> \n",
       "        <td id=\"T_28f27952_5d01_11e9_a3bf_701ce71031efrow1_col2\" class=\"data row1 col2\" >3750</td> \n",
       "        <td id=\"T_28f27952_5d01_11e9_a3bf_701ce71031efrow1_col3\" class=\"data row1 col3\" >20000</td> \n",
       "        <td id=\"T_28f27952_5d01_11e9_a3bf_701ce71031efrow1_col4\" class=\"data row1 col4\" >50000</td> \n",
       "        <td id=\"T_28f27952_5d01_11e9_a3bf_701ce71031efrow1_col5\" class=\"data row1 col5\" >4161</td> \n",
       "        <td id=\"T_28f27952_5d01_11e9_a3bf_701ce71031efrow1_col6\" class=\"data row1 col6\" >3.80%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_28f27952_5d01_11e9_a3bf_701ce71031efrow2_col0\" class=\"data row2 col0\" >本科</td> \n",
       "        <td id=\"T_28f27952_5d01_11e9_a3bf_701ce71031efrow2_col1\" class=\"data row2 col1\" >16265</td> \n",
       "        <td id=\"T_28f27952_5d01_11e9_a3bf_701ce71031efrow2_col2\" class=\"data row2 col2\" >4500</td> \n",
       "        <td id=\"T_28f27952_5d01_11e9_a3bf_701ce71031efrow2_col3\" class=\"data row2 col3\" >14583</td> \n",
       "        <td id=\"T_28f27952_5d01_11e9_a3bf_701ce71031efrow2_col4\" class=\"data row2 col4\" >37500</td> \n",
       "        <td id=\"T_28f27952_5d01_11e9_a3bf_701ce71031efrow2_col5\" class=\"data row2 col5\" >71774</td> \n",
       "        <td id=\"T_28f27952_5d01_11e9_a3bf_701ce71031efrow2_col6\" class=\"data row2 col6\" >65.63%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_28f27952_5d01_11e9_a3bf_701ce71031efrow3_col0\" class=\"data row3 col0\" >大专</td> \n",
       "        <td id=\"T_28f27952_5d01_11e9_a3bf_701ce71031efrow3_col1\" class=\"data row3 col1\" >12473</td> \n",
       "        <td id=\"T_28f27952_5d01_11e9_a3bf_701ce71031efrow3_col2\" class=\"data row3 col2\" >4500</td> \n",
       "        <td id=\"T_28f27952_5d01_11e9_a3bf_701ce71031efrow3_col3\" class=\"data row3 col3\" >12000</td> \n",
       "        <td id=\"T_28f27952_5d01_11e9_a3bf_701ce71031efrow3_col4\" class=\"data row3 col4\" >27500</td> \n",
       "        <td id=\"T_28f27952_5d01_11e9_a3bf_701ce71031efrow3_col5\" class=\"data row3 col5\" >33187</td> \n",
       "        <td id=\"T_28f27952_5d01_11e9_a3bf_701ce71031efrow3_col6\" class=\"data row3 col6\" >30.35%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x12c33f969e8>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_edu=get_sub_stats_by_col(data[data.edu.isin(['大专','本科','硕士','博士'])], 'edu')\n",
    "apply_style(data_edu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "salary_associate=data[data.edu=='大专'].monthly_salary\n",
    "salary_bachelor=data[data.edu=='本科'].monthly_salary\n",
    "salary_master=data[data.edu=='硕士'].monthly_salary\n",
    "salary_phd=data[data.edu=='博士'].monthly_salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "大专，本科，硕士，博士的平均工资分别是12667，16633，22588，29686。中位数分别是12500， 15000，20000， 29000。\n"
     ]
    }
   ],
   "source": [
    "print('大专，本科，硕士，博士的平均工资分别是{:.0f}，{:.0f}，{:.0f}，{:.0f}。中位数分别是{:.0f}， {:.0f}，{:.0f}， {:.0f}。'.format(\n",
    "    salary_associate.mean(),salary_bachelor.mean(),salary_master.mean(),salary_phd.mean(),\n",
    "    salary_associate.median(),salary_bachelor.median(),salary_master.median(),salary_phd.median()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=23.144740567704, pvalue=1.6545088803207718e-06)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_phd, salary_master)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=595.5576201244492, pvalue=5.9379062255845094e-130)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_master, salary_bachelor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=1771.1009187651957, pvalue=0.0)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_bachelor, salary_associate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "经过Oneway Anova Test，证明从大专到博士，学历每提高一级，工资都有显著的提高。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "order=['大专','本科','硕士','博士']\n",
    "a=sns.boxplot(y='edu',x='monthly_salary',order=order,data=data[data.edu.isin(order)], orient='h')\n",
    "plt.annotate('https://github.com/juwikuang/job_survey', xy=(0.5,1.5), xytext=(2, 1.55))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 工作经验 Working Experience"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_68f93bba_5d01_11e9_b6ce_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"col_heading level0 col0\" >experience</th> \n",
       "        <th class=\"col_heading level0 col1\" >salary_mean</th> \n",
       "        <th class=\"col_heading level0 col2\" >salary_95_min</th> \n",
       "        <th class=\"col_heading level0 col3\" >salary_median</th> \n",
       "        <th class=\"col_heading level0 col4\" >salary_95_max</th> \n",
       "        <th class=\"col_heading level0 col5\" >head_count</th> \n",
       "        <th class=\"col_heading level0 col6\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <td id=\"T_68f93bba_5d01_11e9_b6ce_701ce71031efrow0_col0\" class=\"data row0 col0\" >10+</td> \n",
       "        <td id=\"T_68f93bba_5d01_11e9_b6ce_701ce71031efrow0_col1\" class=\"data row0 col1\" >32065</td> \n",
       "        <td id=\"T_68f93bba_5d01_11e9_b6ce_701ce71031efrow0_col2\" class=\"data row0 col2\" >12398</td> \n",
       "        <td id=\"T_68f93bba_5d01_11e9_b6ce_701ce71031efrow0_col3\" class=\"data row0 col3\" >30000</td> \n",
       "        <td id=\"T_68f93bba_5d01_11e9_b6ce_701ce71031efrow0_col4\" class=\"data row0 col4\" >54167</td> \n",
       "        <td id=\"T_68f93bba_5d01_11e9_b6ce_701ce71031efrow0_col5\" class=\"data row0 col5\" >393</td> \n",
       "        <td id=\"T_68f93bba_5d01_11e9_b6ce_701ce71031efrow0_col6\" class=\"data row0 col6\" >0.30%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_68f93bba_5d01_11e9_b6ce_701ce71031efrow1_col0\" class=\"data row1 col0\" >5_10</td> \n",
       "        <td id=\"T_68f93bba_5d01_11e9_b6ce_701ce71031efrow1_col1\" class=\"data row1 col1\" >21918</td> \n",
       "        <td id=\"T_68f93bba_5d01_11e9_b6ce_701ce71031efrow1_col2\" class=\"data row1 col2\" >10500</td> \n",
       "        <td id=\"T_68f93bba_5d01_11e9_b6ce_701ce71031efrow1_col3\" class=\"data row1 col3\" >20000</td> \n",
       "        <td id=\"T_68f93bba_5d01_11e9_b6ce_701ce71031efrow1_col4\" class=\"data row1 col4\" >45000</td> \n",
       "        <td id=\"T_68f93bba_5d01_11e9_b6ce_701ce71031efrow1_col5\" class=\"data row1 col5\" >16264</td> \n",
       "        <td id=\"T_68f93bba_5d01_11e9_b6ce_701ce71031efrow1_col6\" class=\"data row1 col6\" >12.32%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_68f93bba_5d01_11e9_b6ce_701ce71031efrow2_col0\" class=\"data row2 col0\" >3_5</td> \n",
       "        <td id=\"T_68f93bba_5d01_11e9_b6ce_701ce71031efrow2_col1\" class=\"data row2 col1\" >16606</td> \n",
       "        <td id=\"T_68f93bba_5d01_11e9_b6ce_701ce71031efrow2_col2\" class=\"data row2 col2\" >7500</td> \n",
       "        <td id=\"T_68f93bba_5d01_11e9_b6ce_701ce71031efrow2_col3\" class=\"data row2 col3\" >15000</td> \n",
       "        <td id=\"T_68f93bba_5d01_11e9_b6ce_701ce71031efrow2_col4\" class=\"data row2 col4\" >36296</td> \n",
       "        <td id=\"T_68f93bba_5d01_11e9_b6ce_701ce71031efrow2_col5\" class=\"data row2 col5\" >38229</td> \n",
       "        <td id=\"T_68f93bba_5d01_11e9_b6ce_701ce71031efrow2_col6\" class=\"data row2 col6\" >28.95%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_68f93bba_5d01_11e9_b6ce_701ce71031efrow3_col0\" class=\"data row3 col0\" >no</td> \n",
       "        <td id=\"T_68f93bba_5d01_11e9_b6ce_701ce71031efrow3_col1\" class=\"data row3 col1\" >13714</td> \n",
       "        <td id=\"T_68f93bba_5d01_11e9_b6ce_701ce71031efrow3_col2\" class=\"data row3 col2\" >3000</td> \n",
       "        <td id=\"T_68f93bba_5d01_11e9_b6ce_701ce71031efrow3_col3\" class=\"data row3 col3\" >12500</td> \n",
       "        <td id=\"T_68f93bba_5d01_11e9_b6ce_701ce71031efrow3_col4\" class=\"data row3 col4\" >35000</td> \n",
       "        <td id=\"T_68f93bba_5d01_11e9_b6ce_701ce71031efrow3_col5\" class=\"data row3 col5\" >39223</td> \n",
       "        <td id=\"T_68f93bba_5d01_11e9_b6ce_701ce71031efrow3_col6\" class=\"data row3 col6\" >29.70%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_68f93bba_5d01_11e9_b6ce_701ce71031efrow4_col0\" class=\"data row4 col0\" >1_3</td> \n",
       "        <td id=\"T_68f93bba_5d01_11e9_b6ce_701ce71031efrow4_col1\" class=\"data row4 col1\" >12662</td> \n",
       "        <td id=\"T_68f93bba_5d01_11e9_b6ce_701ce71031efrow4_col2\" class=\"data row4 col2\" >5250</td> \n",
       "        <td id=\"T_68f93bba_5d01_11e9_b6ce_701ce71031efrow4_col3\" class=\"data row4 col3\" >11500</td> \n",
       "        <td id=\"T_68f93bba_5d01_11e9_b6ce_701ce71031efrow4_col4\" class=\"data row4 col4\" >28018</td> \n",
       "        <td id=\"T_68f93bba_5d01_11e9_b6ce_701ce71031efrow4_col5\" class=\"data row4 col5\" >37951</td> \n",
       "        <td id=\"T_68f93bba_5d01_11e9_b6ce_701ce71031efrow4_col6\" class=\"data row4 col6\" >28.74%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x12c315eee80>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_experience=get_sub_stats_by_col(data, 'experience')\n",
    "apply_style(data_experience)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "salary_we_10=data[data.experience=='10+'].monthly_salary\n",
    "salary_we_5_10=data[data.experience=='5_10'].monthly_salary\n",
    "salary_we_3_5=data[data.experience=='3_5'].monthly_salary\n",
    "salary_we_1_3=data[data.experience=='1_3'].monthly_salary\n",
    "salary_we_no=data[data.experience=='no'].monthly_salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sGMcffzxr167lwQcf5MEHHwRg6dKlTJ06FYA//OEPDB06lPLycj777LOU43T77bdz+umnM2jQINatWwfAjBkzOP300znzzDPZuHEjl112GRdeeCEDBgzgggsu4JJLLml0eevr66MfqlOnTo0muEisJSUlfPTRR3Tv3p0tW7Zw9tlnJx2DiNdff52hQ4fy2WefJW1v165d/OAHP2DIkCFcfvnl0X5vueWW6DrwwQcfsHfvXsrLyxkwYAAXX3wxd9xxR7SP+++/nyuuuCKu39jx3LFjB+eccw5DhgxhwoQJ0To33ngjgwcP5qc//WnKMd67dy8jR44kGAxy3nnnsX///qTLEVn+//qv/+L73/9+9P/z5z//Ofp/efTRR9mzZw+jR48mGAxy7bXXAjBlyhQeeeSR6LJHnos0ptHkYGYzw3+XmNmz4ccSM3s2O+FlxoYNG1i2bBnnn38+ZWVlTJo0iUmTJvG///u/QOiNllgGMGTIEJ577jm6d+/OE088wY4dO1izZg3PPfcclZWVccnhsccei+tz//79vPHGG/Tr1y9l2ccff8xHH33E8uXLeffdd5k5c2bS+K+77jpmzZrFZZddxtKlSzniiCMAeOWVV1i4cCGTJk1i/vz5KZe/uLiYJUuWEAwGuf/++5PWWbNmDS+88AJ/+9vfmDVrFitWrOC1115j6dKlvPjii1RWVnLjjTcCcO2113LQQQdx11138f7776dcXj+OPfZYFi5cyGmnncaiRYvo379/yrpbtmzhkksuYe7cuXzta19LWWfcuHE888wz1NfX8+GHHwLx68Czzz7Lm2++SXFxMS+88AIbNmzg5ptvBmDnzp188cUXdO/ePWUcd9xxBxdddBHPP/88O3fu5K9//SsAI0aM4MUXX2TdunUp90LXrVtHUVERy5Yt4/LLL2fXrl0p+1m+fDmDBg1i4cKFAFx44YUsWLAAgGXLlnH22Wcze/Zs+vbty7Jly9iyZQtr1qxh7Nix/OEPfwBg4cKFnHvuuSn7EIloNDk45yaG/w51zg0LP4Y654ZlJ7zMGDt2LABHH300X3zxhe/5TjnlFAD69etHfX09Xbt25bLLLuMHP/gBDzzwQMoPKICHH344bqs6Wdmhhx7K559/zoABAzx1/fj3f/93OnTokHS59u7dG30e+Zqnf//+vP3220nbevPNNykpKYnWHzt2LG+88QannnoqZsbAgQN54403AAgEArRr145AIEBRUVHK5W1MJL7+/fszb948RowYwaOPPhod82TuvvtuiouL2bhxY8r2OnTowH333ccll1zCjh07ouWJ60CPHj1YuXIlwWCQ6667LtrOvffey49+9KNGY1+3bl10TAcMGBAdFz/j3L9/f/r27cvw4cNZuHAhhx56aNLlAOjbt2/cHmnv3r3ZtGkTn376KV26dKFTp0689dZbPP7445SWlvLOO++wefNmjjvuOD777DOWLl1K37596dixY6PLIwJt9GulTp06xU137NiRPXv2AEQOvictW7FiBQCrVq2iZ8+evP/++3Tt2pWFCxfSo0cPz95CxOeff877779Pr169Gi1bsWIF5513Hi+//DLXX399o8uQLL7E5TrooIOiXzlFtjABVq5cCYT2DgKBQNL2+/TpE6334osvcumll3L88cfzyiuv4Jxj+fLlnHDCCb6XN5nY+CJb2yeffDLPPvssZ511FgsXLmx0z6GyspJ77rmHysrKlO3df//9jB49mrlz58aNT+JY/fWvf6WyspKXXnopmtS2bt1Khw4dOPzwwxtdjhNOOIHly5cDxI2Ln3F+9dVXGTx4MIsWLeLjjz/m+eefT7ocAJ07e097Pu2005g1axajRo0C4Nvf/jYTJkxg6dKlTJ8+PXqc5KKLLuKKK66IJkWRprTJ5JCorKyMxx57jMGDB/P888+nLHvllVcoLS3lk08+YeTIkRx11FE8+eSTDB48OPqBFhG7hffAAw94Dl4mK+vTpw8zZ85k6NChXHDBBdF+kzn55JN56623GDJkCH/84x+T1hk2bBhPPvkk1157LV9++dU9E+vq6igtLWXx4sVceeWVnnghtHc0aNAgBg8ezC233MKUKVPo27cvpaWlDB48mKqqKmbMmJG032TLFrF3714OPvhgAEaNGsWvf/1r/uM//oOuXbsCoS3pY445hmOPPZYjjzySY445JuUYHHLIIXzzm9+kT58+zJ8/P2l7ZWVlzJgxg2HDQju7mzdvTtrWySefzLhx4xg2bBgXXXQRa9eu5b777ku51xC7HDfddBOPPPIIZ5xxBl26dGH48OEA/OlPf2Lw4MF861vfSrkHFAgEqK6u5vTTT+eDDz6gpKQk6XKkcuGFFzJr1ixGjhwJwFVXXcWCBQsIBoP89re/5Zvf/CYAo0ePxsw444wzGm1PJMrPKU3Aaj/1svlo6amsLZXsFFi/PvzwQ19lTz31lAsGg+6ss85y55xzjps7d26L+su1ZMv2+eefuwEDBrhBgwa55557LgdRNW727Nlu6NChbvjw4W7EiBFuyZIlSZfj3XffdQMHDnSDBw9269aty0Gkzbd27Vp36qmnuvvuuy/XoUgewOeprOZ8nJVqZhOABudcdYZzlW8lJSWutrY212GI+JZ4ttVhhx3GE088kZtgpM0ys5XOuZKm6vm9QvpcQldHXwzsBZwr8IPS6VRdXc2GDRui05s2bQJCZwUl07NnT/2mcxuU79eliMTy+xvSQzMdSCHbsGEDq15fBV3CBeEzWrfaVm/lT7IWlohIi/lKDmZmwAigO7AO2Oic+0cmAys4XaChtAGAoqWh4/yR6ViR10RE8plu2S0iIh5+k8MRzrnrgV3OuRebMZ+IiBQgvx/ykVt2f0O37BYROfD5PSB9tZmdC7wZfkzLaFQiIpJTvvYcwr/8VgTsAw6o23WLiIiX36+VHgGGAbuBs4HfZywiERHJOb8XwR3pnBsTmTCzJRmKR0RE8oDf5LDHzCYBK4HTgJ1mFnTOLctcaCIikit+v1Z6GTgYOJ1QQlkFlGYopqyrrq6mujpvbhvVqEKKVUQKl989h/ucc9F7HZvZSc659P7Acg7F3hcp3xVSrCJSuPzuOTxqZj8zs38xs18AdzQ5h4iIFCy/yWEw8C1gK6GrpM9uor6IiBSw5txb6RBCxxy+a2Z3Zy4kERHJNb/HHH5F6EbUPYBbgNQ/7CsiIgXPb3K4CPgGoa+WKoEx6M6sIiIHLL/J4UTnXKmZPeuc+4uZ3dCcTszs68ApwCrn3LZmR5lGc+bM4d57740ra9++PX379s1RRM23efNmgsGgp7xjx47s3bs3On3cccfx9ttvR6d79epFXV1ddPrWW29l2rT422Rdf/31/PKXvyQQCHDXXXfRtWtXT19jxoyhT58+TJs2jdtuu42hQ4cyZcoUlixZQllZGZWVlQCsX7+e6667jp/97Gf84he/4Dvf+Q61tbUEg0F27tzJ1KlT6dq1a1zbEydOpLa2loEDB3LnnXdG2/j1r39Nz549Oe+889i+fTtHHnkkf/rTn1o8hontirTWtm3buO2225Ku14XYj99jDlvN7FbgcDOrAD7w24GZHQ48RejiuSVmdkSSOu3N7D0zWxp+nOi3/eZKTAwA+/fvz1R3GbF1a5JfmIO4xADEJQYgLjEA3HGH96SzmTNn0tDQwDvvvENNTU3SfubNmxedt6qqCoAlS0IXzS9evDhab/r06ezevZvbb7+d3bt3E/nN72XLlrFmzZqk7UfqLF++PK6NSBLbvn07AB999FHS2PxKbFektWpqalKu14XYj9/kMJbQMYeXgMOAy5vRRz9gonPudmAhyY9X9APmOudKw4/XmtG+b3PmzEn52tq1azPRZdpt3ry56Uo+JUuKzn11X8Wnnnoq6R5K7Lz79+/nmmuuiXutqqqK9evXU19f32g/CxYsiH7YQ2ivIda1114bbaO+vp5Ro0bFvT569OgUS9a42Njq6+t17Yi02rZt21iwYEHS9boQ+wGw2A+DTDKzIDAdGOmc+zThtf8EriV0Y7/XgB875xrdnC8pKXGRrUy/Un3QRZx00knNai+irq6OXQ27aBjp42dCnyqic1FnevXq1aK+Vq8ujGsPA4FA9AM4lQ4dOjBixIhoUmjq/5PMsmXNv4PL2LFj42ILBAI89NBDzW5HJOKuu+7i6aefZt++fZ71Ot/6MbOVzrmSpupl5Rfdwr9B/UPgY0K3/U70CnCWc+40oAOhO78ma+dqM6s1s9pUX61IfmgqMQDs27ePRYsWZT6YBImx+YlVpDGLFy9m377QR1sm1+ts9QP+D0i3igvtnlxrZlXAKELXTcRa45z7PPy8Fki6We2cmw3MhtCeQ7rjbOk9i8aPH8+qzav8Ve4MvXr0anFfLdm6zgW/ew7Dhw/PTkAxEmMLBAJZj0EOLGVlZXFb9Jlar7PVD2Rhz8HMbjSzseHJLsAnSarNMbPvmlk74N+AVzMRy1VXXZXytfbts5InW+2IIzzH8zPG75iccMIJcdNlZWVMnjy5yfmKioqoqKiITpeUxO/pnnhi/HkJXbp0iZs+8sgjfcWXKDG2W2+9tUXtiERUVFQQ+oLEu14XYj+Qna+VZgOXmtkyoB2QbD9oGjAHWA285Jx7JhOBXHrppSlfK5RTWXv06JG2tpJ9+EdWPICRI0em/E4/Mm/79u2555574l6rrKykd+/e0S3yVP2Ul5fHnYo3c+bMuDq/+c1vom0EAgHmz58f93pLT2WNjS0QCOhUVmm1bt26UV5ennS9LsR+IAvJwTn3sXOuzDkXdM79p0tyBNw5t9Y51885d6Jz7pZMxpNs76FQ9hoiUu09dOzYMW76uOOOi5tOPAh+8803e9qYOHEiRUVFHHvssSm3SsaMGROdN3JNw9ChQ4HQXkPE5MmT6dSpE7fccgudOnWK7hkEg0H69euXtP1InYEDB8a1Edm6j7wZWrrXkBib9hokXSoqKlKu14XYT9bOVop2aLY0oWinc+7c5rbTkrOVUhk/fjzQ+mMOkbOTGj1baWkRJ/c4uVV9tSZWEWnb/J6tlPVNZudcabb7FBGR5snKqawiIlJYlBxERMRDyUFERDyUHERExKOwzuHMkEI6z72QYhWRwqXkwFenhxaCQopVRAqXvlYSEREPJQcREfFQchAREQ8lBxER8VByEBERDyUHERHxUHIQEREPJQcREfHQRXDp8slXv+MQ+SHU6HRCPdL3Y24iIhmh5JAGibe02OQ2AVDco9hbuYdugSEi+U/JIQ10SwsROdDomIOIiHgoOYiIiIeSg4iIeCg5iIiIh5KDiIh4KDmIiIiHkoOIiHgoOYiIiIcugvOpurqaDRs2xJVt2hS+ErrYeyV0z549dXGciBQsJQefNmzYwJurV3NUTNln4b+fbNsWV/eDrEUlIpIZSg7NcBTwIyw6fT8OEspiy0VECpWOOYiIiIeSg4iIeCg5iIiIh5KDiIh4KDmIiIiHkoOIiHgoOYiIiIeSg4iIeLS55FBdXU11dXWuw2hUIcQoIge2NneFdOL9kfJRIcQoIge2NrfnICIiTVNyEBERDyUHERHxUHIQEREPJQcREfFQchAREY+8Sg5m9q9mdpaZfS3XseTSjh07WL16NcFgsNmP8847L/r84osvjntt5cqVcdMRjz/+eLT+9u3b2bZtG+PGjePhhx8mGAxywQUXEAwGGTt2LOXl5UlPtZ01axbBYJC7777b81qkve3btze57Il1mzNvS/orFOvXr0859umUjrHL5vino68VK1ZQWlrKypUr0xiZV6Gtl1lLDmbW3cyeb+T13sAfgcHAc2Z2ULZiyzfvvfdei+eNXfEiv3EdUVlZmXSeWbNmRevX1NRQU1PDmjVrmD17NgBbt24FoL6+nt27dzNt2jRPG4899hgA8+bN87wWaa+mpqbJ+BPrNmfelvRXKKZPn55y7NMpHWOXzfFPR19Tp06loaEh5fsjXQptvcxKcjCzw4EaoFMj1foBlzvnbgPeAb6VjdjyzTPPPJOxtnft2hU3HQwGefzxx3Huq581nT9/PgsWLIgrS1RfXx+3BRtJLhGxew/btm2LtrdgwYJGt5oS69bV1fmetyX9FYqtT1whAAAKY0lEQVT169dTX18PeMc+ndIxdtkc/3T0tWLFiuj7YteuXRnbeyjE9TJbV0h/CfwQeCJVBefcn8ysvZmNAA4HMvIO2LRpE3v37mX8+PHNmq+urs53Jt0ObK2ra3YfAKtXr272PK2R+MHe0NDAvn37mpxv2rRpPPTQQ8BXew0R8+bN4yc/+QkQ2lqKJJqGhgZqamqYOHFi0jYT61ZVVfmeN1UbfubJd9OnT4+bjh37dErH2GVz/NPR19SpU+OmKysrefrpp9MVYlQhrpdZ2XNwzn3qnNvpo2pnYAywEfBsuprZ1WZWa2a1ka86pHWS7SE0ttcQEdmSbcrixYujyWbfvn0sWrTId936+nrf87akv0KRONZ+x7650jF22Rz/dPSVuDedOJ0uhbhe5tW9lZxznwAVZjYHOBV4OeH12cBsgJKSkqY/wZIoLi4GaPaN7caPH88nPrfquwJdevVq0c3zhg0bxv79+5s9X0uZmScZJCtLFAgEfLVfVlbG008/zb59++jQoQPDhw/3XbdHjx5s3rzZ17wt6a9QBAKBuITgd+ybKx1jl83xT0dfnTt3jksInTt3TmeIUYW4XubN2Upmdo+ZRU6h6QJ8kst4cuXmm2/Oan8TJkyImy4qKqJDhw5NznfrrbdGn59//vlxr40ZMyb6vKKiAjOLtl1RUZGyzcS6lZWVvudtSX+FYvLkyXHTsWOfTukYu2yOfzr6SvxaqaqqKh2heRTiepk3yQG4E7gjfEbTCufcW7kOKBfOOuusjLWduFW0bNkyzjvvvOhKCzBq1CjKy8vjyhIFAgF69uwZnU5MMJHjDQDdunWLtldeXk7Xrl1TtptYt1evXr7nbUl/haJ3797RvYXEsU+ndIxdNsc/HX2ddtpp0fdF586dOeWUU9IdJlCY62VWk4NzrrSR1951zp3hnBvinMtM+i4QRx99dIvnjV3pIl+hRaTaKop8uBcXF1NRUUFFRQX9+vXj6quvBuCII44AQh9MnTp1SrrlGtl7iN1riIi053fLP7Zuc+ZtSX+FYvLkySnHPp3SMXbZHP909DV16lSKiooyttcQUWjrpfk5+JjWDs2OAh5JKH7LOffj5rRTUlLiamtrm91/5Ayilh5z+BFfbVHfHz5mHlsWKe9y0kkt/sGelsYoItIUM1vpnCtpql7WD0g75z4ASrPdr4iI+JdPxxxERCRPKDmIiIiHkoOIiHgoOYiIiEdeXSGdDZk6RzydCiFGETmwtbnk0JKb4WVbIcQoIgc2fa0kIiIeSg4iIuKh5CAiIh5KDiIi4qHkICIiHkoOIiLioeQgIiIebe46h9b4gK9u0w2wJfz3/oSfu/6A0E/ZiYgUKiUHn5Jdtbxr0yYAuiT8qE6XFPVFRAqFkoNPumpZRNoSHXMQEREPJQcREfFQchAREQ8lBxER8TDnXNO18pCZbQU2tmDWbsC2NIeTKYUUKyjeTCukeAspVmhb8R7jnDuiqUoFmxxaysxqnXMluY7Dj0KKFRRvphVSvIUUKyjeZPS1koiIeCg5iIiIR1tMDrNzHUAzFFKsoHgzrZDiLaRYQfF6tLljDiIi0rS2uOcgIiJNUHIQERGPNpUczOx+M3vJzCbnqP/DzGyBmS0ys8fN7CAze8/MloYfJ4br3WZmr5jZb2Lm9VWW5njbJ8bXmtiyEO81MbGuDv+/8258zay7mT0fft7BzJ40sxfN7IpMlKU53qPDY/msmc22kB5mtilmnI8I1/W83/yWpTHetMeW4Xhvi4n1TTO7KVfj22aSg5mdD7Rzzg0CjjWzXjkI4xJgpnNuOKGffZgEzHXOlYYfr5nZKcAZwGnAR2Z2lt+yDMTbLzY+4KCWxpaNeJ1z98TE+jzwO/JsfM3scKAG6BQuGgesdM4NBkab2dcyUJbOeH8MXOOcGwZ8EzgRGADcHjPOW5O93/yWpTnetMaW6Xidc1Ni1uG1wEPpXga/sbWZ5ACUAvPCzxcReuNnlXPu/znnFocnjwD2AyPNbEU4u7cHvgf8jwudKbAQGNKMsnQbGBsfcGYrYstGvEBoaxHoDpSQf+P7JfBD4NPwdClfrZfLwjGnuyxt8TrnbnHOvRF+rSuhq3QHAlea2d/N7I4kyxV5v/ktS1u8GYgt0/ECYGanApucc5szsAy+tKXk0AnYHH6+g9CHR06Y2SDgcGAxcJZz7jSgA3A2yeP0W5ZuryTE17EVsWVz/K8F7kkSf87H1zn3qXNuZ0xRa+LJeNxJ4gXAzH4IvO6c+wewgNCH0KnAIDPrl0fxpju2rIwvcB3w6wwtgy9t6cd+dhH6cAPoTI4So5l9ndA//QLgA+fc5+GXaoFeJI/Tb1m6rUmIL5IgWhJbVsbfzIqAocAtwEF5Pr7E9LMz3M+uDJSllZkdC/wMiHzV9rfIOJvZKvJrnNMdW8bXCzPrAhzpnHs7Q8vgS1vac1jJV7tU3wXqsx2AmR0EPArc5JzbCMwxs++aWTvg34BXU8TptyzdEuPr1IrYsjX+Q4CXw18H5fv40sp4sh53+DvyucAVMVu8C83sX83sUGA4oe/K8yLeDMSWjfXiXODpmOncjK9zrk08gH8h9OEwE3gDOCwHMVwDfAwsDT+mAGuA1wgdcIJQwn4R+BXwFvAtv2UZiLdvbHytiS0b8YZjvgM4P1n8+TS+wNLw32OA18P9vAK0S3dZmuP9P8CWmHX4e4T21N4Mj/VPUr3f/JalOd60xpbpeMPP/wD0j5nOyfim/c2Zzw9C3/OPAY7KdSxNxNkRGA0c29yyfI4tF/EWwvgC3wivl4dlqixH4+x5v/kty+fY8uVzJNPx6vYZIiLi0ZaOOYiIiE9KDiIi4qHkICIiHkoOIoCZlZpZIGb6wdjphLpLsxBPxvsQaYySg0hIKRDIcQwieUPJQQ4YZrbSQne9fcLMXjazmy10h9LnzWxWuM6DZnZruOxvZtbRzB4ALgNmmdnvY5ocG1svRZ81ZjYwpu2BKeodaWZLzOwFM/tduOwb4ennzez2RpbLU8/MAmb2ezN7IBy/71hE/FBykAPJocCFhO4mezGh2ww84pwbAhxmZj8I1+scLnsTONk5dznwIDDBOXdJTHtx9VL0+RBwSfjq9+8455anqDcEeM05dwawLHybjx6E7sxbDpzTyHKlqncO8Ltw/M2JRaRJSg5yIPnQObcL2EjobpfdgZfDr70MfCf8vCb89z1CtyFPxU+9JcAgYAQwv5G2FgDtzGwx0Mc510DorryTgPuAxm6tnareooQE4DcWkSYpOciBbDeh2x0T/vt6THmivYT2PDAza6RenPCH/GLgF8DDjVQdBMxxzpUBw8zsOGAiMAO4EmjsatRU9eJuqteMWESapOQgB7KDgYvM7AXgE+fcokbq/g8wycyWA8c1s59Hgfdc6GaKqbwD3GlmLwEfEdq7eQr4LaGt/D3h36FIxm89v7GINEm3zxBpBTM7E7gTuMU591fFIgcKJQeRNDKzo4BHEorfcs79OBfxiLSUkoOIiHjomIOIiHgoOYiIiIeSg4iIeCg5iIiIh5KDiIh4/H8Cw5DkA9qGuAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "order=['10+','5_10','3_5','1_3']\n",
    "sns.boxplot(y='experience',x='monthly_salary',order=order,data=data[data.experience.isin(order)], orient='h')\n",
    "plt.annotate('https://github.com/juwikuang/job_survey', xy=(1.5,1.5), xytext=(1.55, 1.55))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 公司 Company"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 公司性质 Company Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_7f49fd42_5d01_11e9_998d_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"col_heading level0 col0\" >company_type</th> \n",
       "        <th class=\"col_heading level0 col1\" >salary_mean</th> \n",
       "        <th class=\"col_heading level0 col2\" >salary_95_min</th> \n",
       "        <th class=\"col_heading level0 col3\" >salary_median</th> \n",
       "        <th class=\"col_heading level0 col4\" >salary_95_max</th> \n",
       "        <th class=\"col_heading level0 col5\" >head_count</th> \n",
       "        <th class=\"col_heading level0 col6\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow0_col0\" class=\"data row0 col0\" >外资（欧美）</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow0_col1\" class=\"data row0 col1\" >18898</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow0_col2\" class=\"data row0 col2\" >4500</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow0_col3\" class=\"data row0 col3\" >17500</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow0_col4\" class=\"data row0 col4\" >45000</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow0_col5\" class=\"data row0 col5\" >7795</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow0_col6\" class=\"data row0 col6\" >5.90%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow1_col0\" class=\"data row1 col0\" >合资</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow1_col1\" class=\"data row1 col1\" >16084</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow1_col2\" class=\"data row1 col2\" >5250</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow1_col3\" class=\"data row1 col3\" >14500</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow1_col4\" class=\"data row1 col4\" >35417</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow1_col5\" class=\"data row1 col5\" >12957</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow1_col6\" class=\"data row1 col6\" >9.81%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow2_col0\" class=\"data row2 col0\" >事业单位</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow2_col1\" class=\"data row2 col1\" >15778</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow2_col2\" class=\"data row2 col2\" >4667</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow2_col3\" class=\"data row2 col3\" >12500</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow2_col4\" class=\"data row2 col4\" >29167</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow2_col5\" class=\"data row2 col5\" >950</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow2_col6\" class=\"data row2 col6\" >0.72%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow3_col0\" class=\"data row3 col0\" >外资（非欧美）</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow3_col1\" class=\"data row3 col1\" >15025</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow3_col2\" class=\"data row3 col2\" >3750</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow3_col3\" class=\"data row3 col3\" >12500</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow3_col4\" class=\"data row3 col4\" >35000</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow3_col5\" class=\"data row3 col5\" >7577</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow3_col6\" class=\"data row3 col6\" >5.74%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow4_col0\" class=\"data row4 col0\" >民营公司</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow4_col1\" class=\"data row4 col1\" >14997</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow4_col2\" class=\"data row4 col2\" >4250</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow4_col3\" class=\"data row4 col3\" >12500</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow4_col4\" class=\"data row4 col4\" >35000</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow4_col5\" class=\"data row4 col5\" >92744</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow4_col6\" class=\"data row4 col6\" >70.23%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow5_col0\" class=\"data row5 col0\" >外企代表处</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow5_col1\" class=\"data row5 col1\" >14943</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow5_col2\" class=\"data row5 col2\" >7000</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow5_col3\" class=\"data row5 col3\" >12500</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow5_col4\" class=\"data row5 col4\" >36425</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow5_col5\" class=\"data row5 col5\" >73</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow5_col6\" class=\"data row5 col6\" >0.06%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow6_col0\" class=\"data row6 col0\" >国企</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow6_col1\" class=\"data row6 col1\" >14799</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow6_col2\" class=\"data row6 col2\" >3750</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow6_col3\" class=\"data row6 col3\" >12500</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow6_col4\" class=\"data row6 col4\" >30000</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow6_col5\" class=\"data row6 col5\" >9685</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow6_col6\" class=\"data row6 col6\" >7.33%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow7_col0\" class=\"data row7 col0\" >非营利组织</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow7_col1\" class=\"data row7 col1\" >10814</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow7_col2\" class=\"data row7 col2\" >5929</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow7_col3\" class=\"data row7 col3\" >10000</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow7_col4\" class=\"data row7 col4\" >22308</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow7_col5\" class=\"data row7 col5\" >123</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow7_col6\" class=\"data row7 col6\" >0.09%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow8_col0\" class=\"data row8 col0\" >政府机关</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow8_col1\" class=\"data row8 col1\" >7396</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow8_col2\" class=\"data row8 col2\" >5339</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow8_col3\" class=\"data row8 col3\" >7000</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow8_col4\" class=\"data row8 col4\" >15635</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow8_col5\" class=\"data row8 col5\" >156</td> \n",
       "        <td id=\"T_7f49fd42_5d01_11e9_998d_701ce71031efrow8_col6\" class=\"data row8 col6\" >0.12%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x12c370374e0>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_company_type=get_sub_stats_by_col(data,'company_type')\n",
    "apply_style(data_company_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "us_eu是欧美外企，startup是创业公司，listed是上市公司，state是国企，private是私企，foreign是非欧美外企，其他不足1000个样本的不管了。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 公司规模 Company Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_80463e3a_5d01_11e9_a92f_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"col_heading level0 col0\" >company_size</th> \n",
       "        <th class=\"col_heading level0 col1\" >salary_mean</th> \n",
       "        <th class=\"col_heading level0 col2\" >salary_95_min</th> \n",
       "        <th class=\"col_heading level0 col3\" >salary_median</th> \n",
       "        <th class=\"col_heading level0 col4\" >salary_95_max</th> \n",
       "        <th class=\"col_heading level0 col5\" >head_count</th> \n",
       "        <th class=\"col_heading level0 col6\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow0_col0\" class=\"data row0 col0\" >10000+</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow0_col1\" class=\"data row0 col1\" >20056</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow0_col2\" class=\"data row0 col2\" >4500</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow0_col3\" class=\"data row0 col3\" >17500</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow0_col4\" class=\"data row0 col4\" >50000</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow0_col5\" class=\"data row0 col5\" >5574</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow0_col6\" class=\"data row0 col6\" >4.22%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow1_col0\" class=\"data row1 col0\" >500-1000人</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow1_col1\" class=\"data row1 col1\" >17492</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow1_col2\" class=\"data row1 col2\" >5250</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow1_col3\" class=\"data row1 col3\" >15000</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow1_col4\" class=\"data row1 col4\" >50531</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow1_col5\" class=\"data row1 col5\" >13903</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow1_col6\" class=\"data row1 col6\" >10.53%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow2_col0\" class=\"data row2 col0\" >1000-5000</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow2_col1\" class=\"data row2 col1\" >16852</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow2_col2\" class=\"data row2 col2\" >4000</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow2_col3\" class=\"data row2 col3\" >15000</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow2_col4\" class=\"data row2 col4\" >37500</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow2_col5\" class=\"data row2 col5\" >16254</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow2_col6\" class=\"data row2 col6\" >12.31%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow3_col0\" class=\"data row3 col0\" >5000-10000</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow3_col1\" class=\"data row3 col1\" >15454</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow3_col2\" class=\"data row3 col2\" >5327</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow3_col3\" class=\"data row3 col3\" >14583</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow3_col4\" class=\"data row3 col4\" >30000</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow3_col5\" class=\"data row3 col5\" >3072</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow3_col6\" class=\"data row3 col6\" >2.33%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow4_col0\" class=\"data row4 col0\" ></td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow4_col1\" class=\"data row4 col1\" >15330</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow4_col2\" class=\"data row4 col2\" >3330</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow4_col3\" class=\"data row4 col3\" >12500</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow4_col4\" class=\"data row4 col4\" >50000</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow4_col5\" class=\"data row4 col5\" >804</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow4_col6\" class=\"data row4 col6\" >0.61%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow5_col0\" class=\"data row5 col0\" >150-500</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow5_col1\" class=\"data row5 col1\" >15165</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow5_col2\" class=\"data row5 col2\" >5250</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow5_col3\" class=\"data row5 col3\" >12500</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow5_col4\" class=\"data row5 col4\" >35000</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow5_col5\" class=\"data row5 col5\" >29140</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow5_col6\" class=\"data row5 col6\" >22.07%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow6_col0\" class=\"data row6 col0\" >50-150</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow6_col1\" class=\"data row6 col1\" >14359</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow6_col2\" class=\"data row6 col2\" >4000</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow6_col3\" class=\"data row6 col3\" >12500</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow6_col4\" class=\"data row6 col4\" >33333</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow6_col5\" class=\"data row6 col5\" >40808</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow6_col6\" class=\"data row6 col6\" >30.90%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow7_col0\" class=\"data row7 col0\" >50-</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow7_col1\" class=\"data row7 col1\" >13587</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow7_col2\" class=\"data row7 col2\" >3750</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow7_col3\" class=\"data row7 col3\" >12500</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow7_col4\" class=\"data row7 col4\" >30000</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow7_col5\" class=\"data row7 col5\" >22505</td> \n",
       "        <td id=\"T_80463e3a_5d01_11e9_a92f_701ce71031efrow7_col6\" class=\"data row7 col6\" >17.04%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x12c33f96f28>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_company_size=get_sub_stats_by_col(data,'company_size')\n",
    "apply_style(data_company_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "公司越大，工资越高"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 行业 Industry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_817a2a54_5d01_11e9_af0e_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"col_heading level0 col0\" >industry</th> \n",
       "        <th class=\"col_heading level0 col1\" >salary_mean</th> \n",
       "        <th class=\"col_heading level0 col2\" >salary_95_min</th> \n",
       "        <th class=\"col_heading level0 col3\" >salary_median</th> \n",
       "        <th class=\"col_heading level0 col4\" >salary_95_max</th> \n",
       "        <th class=\"col_heading level0 col5\" >head_count</th> \n",
       "        <th class=\"col_heading level0 col6\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow0_col0\" class=\"data row0 col0\" >finance</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow0_col1\" class=\"data row0 col1\" >16558</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow0_col2\" class=\"data row0 col2\" >5250</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow0_col3\" class=\"data row0 col3\" >15000</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow0_col4\" class=\"data row0 col4\" >35000</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow0_col5\" class=\"data row0 col5\" >4580</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow0_col6\" class=\"data row0 col6\" >3.47%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow1_col0\" class=\"data row1 col0\" >edu</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow1_col1\" class=\"data row1 col1\" >15925</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow1_col2\" class=\"data row1 col2\" >5644</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow1_col3\" class=\"data row1 col3\" >12500</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow1_col4\" class=\"data row1 col4\" >37500</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow1_col5\" class=\"data row1 col5\" >8758</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow1_col6\" class=\"data row1 col6\" >6.63%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow2_col0\" class=\"data row2 col0\" >service</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow2_col1\" class=\"data row2 col1\" >15855</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow2_col2\" class=\"data row2 col2\" >3975</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow2_col3\" class=\"data row2 col3\" >14000</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow2_col4\" class=\"data row2 col4\" >43229</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow2_col5\" class=\"data row2 col5\" >870</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow2_col6\" class=\"data row2 col6\" >0.66%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow3_col0\" class=\"data row3 col0\" >trade</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow3_col1\" class=\"data row3 col1\" >15386</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow3_col2\" class=\"data row3 col2\" >5000</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow3_col3\" class=\"data row3 col3\" >13083</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow3_col4\" class=\"data row3 col4\" >35000</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow3_col5\" class=\"data row3 col5\" >7390</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow3_col6\" class=\"data row3 col6\" >5.60%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow4_col0\" class=\"data row4 col0\" >computer</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow4_col1\" class=\"data row4 col1\" >15300</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow4_col2\" class=\"data row4 col2\" >4000</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow4_col3\" class=\"data row4 col3\" >12500</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow4_col4\" class=\"data row4 col4\" >35000</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow4_col5\" class=\"data row4 col5\" >99456</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow4_col6\" class=\"data row4 col6\" >75.31%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow5_col0\" class=\"data row5 col0\" >ads</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow5_col1\" class=\"data row5 col1\" >15298</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow5_col2\" class=\"data row5 col2\" >5000</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow5_col3\" class=\"data row5 col3\" >12500</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow5_col4\" class=\"data row5 col4\" >35000</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow5_col5\" class=\"data row5 col5\" >1728</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow5_col6\" class=\"data row5 col6\" >1.31%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow6_col0\" class=\"data row6 col0\" >medical</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow6_col1\" class=\"data row6 col1\" >14570</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow6_col2\" class=\"data row6 col2\" >4794</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow6_col3\" class=\"data row6 col3\" >12500</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow6_col4\" class=\"data row6 col4\" >30000</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow6_col5\" class=\"data row6 col5\" >2667</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow6_col6\" class=\"data row6 col6\" >2.02%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow7_col0\" class=\"data row7 col0\" >gov</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow7_col1\" class=\"data row7 col1\" >14482</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow7_col2\" class=\"data row7 col2\" >3750</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow7_col3\" class=\"data row7 col3\" >12500</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow7_col4\" class=\"data row7 col4\" >30896</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow7_col5\" class=\"data row7 col5\" >1717</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow7_col6\" class=\"data row7 col6\" >1.30%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow8_col0\" class=\"data row8 col0\" >logistic</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow8_col1\" class=\"data row8 col1\" >14263</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow8_col2\" class=\"data row8 col2\" >5000</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow8_col3\" class=\"data row8 col3\" >12500</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow8_col4\" class=\"data row8 col4\" >30000</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow8_col5\" class=\"data row8 col5\" >1545</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow8_col6\" class=\"data row8 col6\" >1.17%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow9_col0\" class=\"data row9 col0\" >realestate</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow9_col1\" class=\"data row9 col1\" >13879</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow9_col2\" class=\"data row9 col2\" >3750</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow9_col3\" class=\"data row9 col3\" >12500</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow9_col4\" class=\"data row9 col4\" >32629</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow9_col5\" class=\"data row9 col5\" >1826</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow9_col6\" class=\"data row9 col6\" >1.38%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow10_col0\" class=\"data row10 col0\" >energy</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow10_col1\" class=\"data row10 col1\" >13338</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow10_col2\" class=\"data row10 col2\" >5000</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow10_col3\" class=\"data row10 col3\" >12500</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow10_col4\" class=\"data row10 col4\" >25000</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow10_col5\" class=\"data row10 col5\" >1516</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow10_col6\" class=\"data row10 col6\" >1.15%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow11_col0\" class=\"data row11 col0\" ></td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow11_col1\" class=\"data row11 col1\" >12929</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow11_col2\" class=\"data row11 col2\" >12500</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow11_col3\" class=\"data row11 col3\" >12929</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow11_col4\" class=\"data row11 col4\" >14000</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow11_col5\" class=\"data row11 col5\" >7</td> \n",
       "        <td id=\"T_817a2a54_5d01_11e9_af0e_701ce71031efrow11_col6\" class=\"data row11 col6\" >0.01%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x12c37028c88>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_industry=get_sub_stats_by_col(data,'industry')\n",
    "apply_style(data_industry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
